Fine-Tuning DARTS for Image Classification

Muhammad Suhaib Tanveer, Umar Karim Khan, Chong Min Kyung

Responsive image

Auto-TLDR; Fine-Tune Neural Architecture Search using Fixed Operations

Slides Poster

Neural Architecture Search (NAS) has gained attraction due to superior classification performance. Differential Architecture Search (DARTS) is a computationally light method. To limit computational resources DARTS makes numerous approximations. These approximations result in inferior performance. We propose to fine-tune DARTS using fixed operations as these are independent of these approximations. Our method offers a good trade-off between the number of parameters and classification accuracy. Our approach improves the top-1 accuracy on Fashion-MNIST, CompCars and MIO-TCD datasets by 0.56%, 0.50%, and 0.39%, respectively compared to the state-of-the-art approaches. Our approach performs better than DARTS, improving the accuracy by 0.28%, 1.64%, 0.34%, 4.5%, and 3.27% compared to DARTS, on CIFAR-10, CIFAR-100, Fashion-MNIST, CompCars, and MIO-TCD datasets, respectively.

Similar papers

Operation and Topology Aware Fast Differentiable Architecture Search

Shahid Siddiqui, Christos Kyrkou, Theocharis Theocharides

Responsive image

Auto-TLDR; EDARTS: Efficient Differentiable Architecture Search with Efficient Optimization

Slides Poster Similar

Differentiable architecture search (DARTS) has gained significant attention amongst neural architecture search approaches due to its effectiveness in finding competitive network architectures with reasonable computational complexity. DARTS' search space however is designed such that even a randomly picked architecture is very competitive and due to the complexity of search architectural building block or cell, it is unclear whether these are certain operations or the cell topology that contributes most to achieving higher final accuracy. In this work, we dissect the DARTS's search space as to understand which components are most effective in producing better architectures. Our experiments show that: (1) Good architectures can be found regardless of the search network depth; (2) Seperable convolution is the most effective operation in the search space; and (3) The cell topology also has substantial effect on the accuracy. Based on these insights, we propose an efficient search approach based referred to as eDARTS, that searches on a pre-specified cell with a good topology with increased attention to important operations using a shallow supernet. Moreover, we propose some optimizations for eDARTS which significantly speed up the search as well as alleviate the well known skip connection aggregation problem of DARTS. eDARTS achieves an error rate of 2.53% on CIFAR-10 using a 3.1M parameters model; while the search cost is less than 30 minutes.

E-DNAS: Differentiable Neural Architecture Search for Embedded Systems

Javier García López, Antonio Agudo, Francesc Moreno-Noguer

Responsive image

Auto-TLDR; E-DNAS: Differentiable Architecture Search for Light-Weight Networks for Image Classification

Slides Poster Similar

Designing optimal and light weight networks to fit in resource-limited platforms like mobiles, DSPs or GPUs is a challenging problem with a wide range of interesting applications, {\em e.g.} in embedded systems for autonomous driving. While most approaches are based on manual hyperparameter tuning, there exist a new line of research, the so-called NAS (Neural Architecture Search) methods, that aim to optimize several metrics during the design process, including memory requirements of the network, number of FLOPs, number of MACs (Multiply-ACcumulate operations) or inference latency. However, while NAS methods have shown very promising results, they are still significantly time and cost consuming. In this work we introduce E-DNAS, a differentiable architecture search method, which improves the efficiency of NAS methods in designing light-weight networks for the task of image classification. Concretely, E-DNAS computes, in a differentiable manner, the optimal size of a number of meta-kernels that capture patterns of the input data at different resolutions. We also leverage on the additive property of convolution operations to merge several kernels with different compatible sizes into a single one, reducing thus the number of operations and the time required to estimate the optimal configuration. We evaluate our approach on several datasets to perform classification. We report results in terms of the SoC (System on Chips) metric, typically used in the Texas Instruments TDA2x families for autonomous driving applications. The results show that our approach allows designing low latency architectures significantly faster than state-of-the-art.

OCT Image Segmentation Using NeuralArchitecture Search and SRGAN

Saba Heidari, Omid Dehzangi, Nasser M. Nasarabadi, Ali Rezai

Responsive image

Auto-TLDR; Automatic Segmentation of Retinal Layers in Optical Coherence Tomography using Neural Architecture Search

Poster Similar

Alzheimer’s disease (AD) diagnosis is one of the major research areas in computational medicine. Optical coherence tomography (OCT) is a non-invasive, inexpensive, and timely efficient method that scans the human’s retina with depth. It has been hypothesized that the thickness of the retinal layers extracted from OCTs could be an efficient and effective biomarker for early diagnosis of AD. In this work, we aim to design a self-training model architecture for the task of segmenting the retinal layers in OCT scans. Neural architecture search (NAS) is a subfield of AutoML domain, which has a significant impact on improving the accuracy of machine vision tasks. We integrate the NAS algorithm with a Unet auto-encoder architecture as its backbone. Then, we employ our proposed model to segment the retinal nerve fiber layer in our preprocessed OCT images with the aim of AD diagnosis. In this work, we trained a super-resolution generative adversarial network on the raw OCT scans to improve the quality of the images before the modeling stage. In our architecture search strategy, different primitive operations suggested to find down- \& up-sampling Unet cell blocks and the binary gate method has been applied to make the search strategy more practical. Our architecture search method is empirically evaluated by training on the Unet and NAS-Unet from scratch. Specifically, the proposed NAS-Unet training significantly outperforms the baseline human-designed architecture by achieving 95.1\% in the mean Intersection over Union metric and 79.1\% in the Dice similarity coefficient.

VPU Specific CNNs through Neural Architecture Search

Ciarán Donegan, Hamza Yous, Saksham Sinha, Jonathan Byrne

Responsive image

Auto-TLDR; Efficient Convolutional Neural Networks for Edge Devices using Neural Architecture Search

Slides Poster Similar

The success of deep learning at computer vision tasks has led to an ever-increasing number of applications on edge devices. Often with the use of edge AI hardware accelerators like the Intel Movidius Vision Processing Unit (VPU). Performing computer vision tasks on edge devices is challenging. Many Convolutional Neural Networks (CNNs) are too complex to run on edge devices with limited computing power. This has created large interest in designing efficient CNNs and one promising way of doing this is through Neural Architecture Search (NAS). NAS aims to automate the design of neural networks. NAS can also optimize multiple different objectives together, like accuracy and efficiency, which is difficult for humans. In this paper, we use a differentiable NAS method to find efficient CNNs for VPU that achieves state-of-the-art classification accuracy on ImageNet. Our NAS designed model outperforms MobileNetV2, having almost 1\% higher top-1 accuracy while being 13\% faster on MyriadX VPU. To the best of our knowledge, this is the first time a VPU specific CNN has been designed using a NAS algorithm. Our results also reiterate the fact that efficient networks must be designed for each specific hardware. We show that efficient networks targeted at different devices do not perform as well on the VPU.

Neural Architecture Search for Image Super-Resolution Using Densely Connected Search Space: DeCoNAS

Joon Young Ahn, Nam Ik Cho

Responsive image

Auto-TLDR; DeCoNASNet: Automated Neural Architecture Search for Super-Resolution

Slides Poster Similar

Abstract—The recent progress of deep convolutional neural networks has enabled great success in single image superresolution (SISR) and many other vision tasks. Their performances are also being increased by deepening the networks and developing more sophisticated network structures. However, finding an optimal structure for the given problem is a difficult task, even for human experts. For this reason, neural architecture search (NAS) methods have been introduced, which automate the procedure of constructing the structures. In this paper, we expand the NAS to the super-resolution domain and find a lightweight densely connected network named DeCoNASNet. We use a hierarchical search strategy to find the best connection with local and global features. In this process, we define a complexitybased penalty for solving image super-resolution, which can be considered a multi-objective problem. Experiments show that our DeCoNASNet outperforms the state-of-the-art lightweight superresolution networks designed by handcraft methods and existing NAS-based design.

Stage-Wise Neural Architecture Search

Artur Jordão, Fernando Akio Yamada, Maiko Lie, William Schwartz

Responsive image

Auto-TLDR; Efficient Neural Architecture Search for Deep Convolutional Networks

Slides Poster Similar

Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. These architectures consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time. Thus, significant human effort is necessary to evaluate different trade-offs between depth and performance. To handle this problem, recent works have proposed to automatically design high-performance architectures, mainly by means of neural architecture search (NAS). Current NAS strategies analyze a large set of possible candidate architectures and, hence, require vast computational resources and take many GPUs days. Motivated by this, we propose a NAS approach to efficiently design accurate and low-cost convolutional architectures and demonstrate that an efficient strategy for designing these architectures is to learn the depth stage-by-stage. For this purpose, our approach increases depth incrementally in each stage taking into account its importance, such that stages with low importance are kept shallow while stages with high importance become deeper. We conduct experiments on the CIFAR and different versions of ImageNet datasets, where we show that architectures discovered by our approach achieve better accuracy and efficiency than human-designed architectures. Additionally, we show that architectures discovered on CIFAR-10 can be successfully transferred to large datasets. Compared to previous NAS approaches, our method is substantially more efficient, as it evaluates one order of magnitude fewer models and yields architectures on par with the state-of-the-art.

Channel Planting for Deep Neural Networks Using Knowledge Distillation

Kakeru Mitsuno, Yuichiro Nomura, Takio Kurita

Responsive image

Auto-TLDR; Incremental Training for Deep Neural Networks with Knowledge Distillation

Slides Poster Similar

In recent years, deeper and wider neural networks have shown excellent performance in computer vision tasks, while their enormous amount of parameters results in increased computational cost and overfitting. Several methods have been proposed to compress the size of the networks without reducing network performance. Network pruning can reduce redundant and unnecessary parameters from a network. Knowledge distillation can transfer the knowledge of deeper and wider networks to smaller networks. The performance of the smaller network obtained by these methods is bounded by the predefined network. Neural architecture search has been proposed, which can search automatically the architecture of the networks to break the structure limitation. Also, there is a dynamic configuration method to train networks incrementally as sub-networks. In this paper, we present a novel incremental training algorithm for deep neural networks called planting. Our planting can search the optimal network architecture with smaller number of parameters for improving the network performance by augmenting channels incrementally to layers of the initial networks while keeping the earlier trained parameters fixed. Also, we propose using the knowledge distillation method for training the channels planted. By transferring the knowledge of deeper and wider networks, we can grow the networks effectively and efficiently. We evaluate the effectiveness of the proposed method on different datasets such as CIFAR-10/100 and STL-10. For the STL-10 dataset, we show that we are able to achieve comparable performance with only 7% parameters compared to the larger network and reduce the overfitting caused by a small amount of the data.

Automatic Student Network Search for Knowledge Distillation

Zhexi Zhang, Wei Zhu, Junchi Yan, Peng Gao, Guotong Xie

Responsive image

Auto-TLDR; NAS-KD: Knowledge Distillation for BERT

Slides Poster Similar

Pre-trained language models (PLMs), such as BERT, have achieved outstanding performance on multiple natural language processing (NLP) tasks. However, such pre-trained models usually contain a huge number of parameters and are computationally expensive. The high resource demand hinders their application on resource-restricted devices like mobile phones. Knowledge distillation (KD) is an effective compression approach, aiming at encouraging a light-weight student network to imitate the teacher network, and accordingly latent knowledge is transferred from the teacher to student. However, the great majority of student networks in previous KD methods are manually designed, normally a subnetwork of the teacher network. Transformer is generally utilized as the student for compressing BERT but still contains masses of parameters. Motivated by this, we propose a novel approach named NAS-KD, which automatically generates an optimal student network using neural architecture search (NAS) to enhance the distillation for BERT. Experiment on 7 classification tasks in NLP domain demonstrates that NAS-KD can substantially reduce the size of BERT without much performance sacrifice.

NAS-EOD: An End-To-End Neural Architecture Search Method for Efficient Object Detection

Huigang Zhang, Liuan Wang, Jun Sun, Li Sun, Hiromichi Kobashi, Nobutaka Imamura

Responsive image

Auto-TLDR; NAS-EOD: Neural Architecture Search for Object Detection on Edge Devices

Slides Similar

Model efficiency for object detection has become more and more important recently, especially when intelligent mobile devices are more and more convenient and developed today. Current small models for this task is either extended from the models for classification task, or pruned directly on the basis of large models. These pipelines are not task-specific or data-oriented so that their performance are not good enough for users. In this work, we propose a neural architecture search (NAS) method to build a detection model automatically that can perform well on edge devices. Specifically, the proposed method supports the search of not only multi-scale feature network, but also backbone network. This enables us to search out a global optimal model. To the best of our knowledge, it is a first attempt for searching an overall detection model via NAS. Additionally, we add latency information into the main objective during performance estimation, so that the search process can find a final model suitable for edge devices. Experiments on the PASCAL VOC benchmark indicate that the searched model (named NAS-EOD) can get good accuracy even without ImageNet pre-training. When using ImageNet pre-training, our model is superior to state-of-the-art small object detection models.

Real-time Pedestrian Lane Detection for Assistive Navigation using Neural Architecture Search

Sui Paul Ang, Son Lam Phung, Thi Nhat Anh Nguyen, Soan T. M. Duong, Abdesselam Bouzerdoum, Mark M. Schira

Responsive image

Auto-TLDR; Real-Time Pedestrian Lane Detection Using Deep Neural Networks

Slides Poster Similar

Pedestrian lane detection is a core component in many assistive and autonomous navigation systems. These systems are usually deployed on environments that require real-time processing. Many state-of-the-art deep neural networks only focus on detection accuracy but not inference speed. Hence, without further modifications, they are not suitable for real-time applications. Furthermore, the task of designing a high-performing deep neural network is time-consuming and requires experience. To tackle these issues, we propose a neural architecture search algorithm that can find the best deep network for pedestrian lane detection automatically. The proposed method searches in a network-level space using the gradient descent algorithm. Evaluated on a dataset of 5,000 images, the models derived by the proposed algorithm achieve comparable segmentation accuracy, while being significantly faster than other state-of-the-art methods. The proposed method has been successfully implemented as a real-time pedestrian lane detection tool.

Resource-efficient DNNs for Keyword Spotting using Neural Architecture Search and Quantization

David Peter, Wolfgang Roth, Franz Pernkopf

Responsive image

Auto-TLDR; Neural Architecture Search for Keyword Spotting in Limited Resource Environments

Slides Poster Similar

This paper introduces neural architecture search (NAS) for the automatic discovery of small models for keyword spotting (KWS) in limited resource environments. We employ a differentiable NAS approach to optimize the structure of convolutional neural networks (CNNs) to meet certain memory constraints for storing weights as well as constraints on the number of operations per inference. Using NAS only, we were able to obtain a highly efficient model with 95.6% accuracy on the Google speech commands dataset with 494.8 kB of memory usage and 19.6 million operations. Additionally, weight quantization is used to reduce the memory consumption even further. We show that weight quantization to low bit-widths (e.g. 1 bit) can be used without substantial loss in accuracy. By increasing the number of input features from 10 MFCC to 20 MFCC we were able to increase the accuracy to 96.6% at 340.1 kB of memory usage and 27.1 million operations.

Rethinking of Deep Models Parameters with Respect to Data Distribution

Shitala Prasad, Dongyun Lin, Yiqun Li, Sheng Dong, Zaw Min Oo

Responsive image

Auto-TLDR; A progressive stepwise training strategy for deep neural networks

Slides Poster Similar

The performance of deep learning models are driven by various parameters but to tune all of them every time, for every dataset, is a heuristic practice. In this paper, unlike the common practice of decaying the learning rate, we propose a step-wise training strategy where the learning rate and the batch size are tuned based on the dataset size. Here, the given dataset size is progressively increased during the training to boost the network performance without saturating the learning curve, after certain epochs. We conducted extensive experiments on multiple networks and datasets to validate the proposed training strategy. The experimental results proves our hypothesis that the learning rate, the batch size and the data size are interrelated and can improve the network accuracy if an optimal progressive stepwise training strategy is applied. The proposed strategy also the overall training computational cost is reduced.

Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search

Chu Xiangxiang, Bo Zhang, Micheal Ma Hailong, Ruijun Xu, Jixiang Li, Qingyuan Li

Responsive image

Auto-TLDR; Multi-Objective Neural Architecture Search for Super-Resolution

Slides Poster Similar

Deep convolutional neural networks demonstrate impressive results in the super-resolution domain. A series of studies concentrate on improving peak signal noise ratio (PSNR) by using much deeper layers, which are not friendly to constrained resources. Pursuing a trade-off between the restoration capacity and the simplicity of models is still non-trivial. Recent contributions are struggling to manually maximize this balance, while our work achieves the same goal automatically with neural architecture search. Specifically, we handle super-resolution with a multi-objective approach. We also propose an elastic search tactic at both micro and macro level, based on a hybrid controller that profits from evolutionary computation and reinforcement learning. Quantitative experiments help us to draw a conclusion that our generated models dominate most of the state-of-the-art methods with respect to the individual FLOPS.

Fast and Accurate Real-Time Semantic Segmentation with Dilated Asymmetric Convolutions

Leonel Rosas-Arias, Gibran Benitez-Garcia, Jose Portillo-Portillo, Gabriel Sanchez-Perez, Keiji Yanai

Responsive image

Auto-TLDR; FASSD-Net: Dilated Asymmetric Pyramidal Fusion for Real-Time Semantic Segmentation

Slides Poster Similar

Recent works have shown promising results applied to real-time semantic segmentation tasks. To maintain fast inference speed, most of the existing networks make use of light decoders, or they simply do not use them at all. This strategy helps to maintain a fast inference speed; however, their accuracy performance is significantly lower in comparison to non-real-time semantic segmentation networks. In this paper, we introduce two key modules aimed to design a high-performance decoder for real-time semantic segmentation for reducing the accuracy gap between real-time and non-real-time segmentation networks. Our first module, Dilated Asymmetric Pyramidal Fusion (DAPF), is designed to substantially increase the receptive field on the top of the last stage of the encoder, obtaining richer contextual features. Our second module, Multi-resolution Dilated Asymmetric (MDA) module, fuses and refines detail and contextual information from multi-scale feature maps coming from early and deeper stages of the network. Both modules exploit contextual information without excessively increasing the computational complexity by using asymmetric convolutions. Our proposed network entitled “FASSD-Net” reaches 78.8% of mIoU accuracy on the Cityscapes validation dataset at 41.1 FPS on full resolution images (1024x2048). Besides, with a light version of our network, we reach 74.1% of mIoU at 133.1 FPS (full resolution) on a single NVIDIA GTX 1080Ti card with no additional acceleration techniques. The source code and pre-trained models are available at https://github.com/GibranBenitez/FASSD-Net.

AOAM: Automatic Optimization of Adjacency Matrix for Graph Convolutional Network

Yuhang Zhang, Hongshuai Ren, Jiexia Ye, Xitong Gao, Yang Wang, Kejiang Ye, Cheng-Zhong Xu

Responsive image

Auto-TLDR; Adjacency Matrix for Graph Convolutional Network in Non-Euclidean Space

Slides Poster Similar

Graph Convolutional Network (GCN) is adopted to tackle the problem of the convolution operation in non-Euclidean space. Although previous works on GCN have made some progress, one of their limitations is that their input Adjacency Matrix (AM) is designed manually and requires domain knowledge, which is cumbersome, tedious and error-prone. In addition, entries of this fixed Adjacency Matrix are generally designed as binary values (i.e., ones and zeros) which can not reflect more complex relationship between nodes. However, many applications require a weighted and dynamic Adjacency Matrix instead of an unweighted and fixed Adjacency Matrix. To this end, there are few works focusing on designing a more flexible Adjacency Matrix. In this paper, we propose an end-to-end algorithm to improve the GCN performance by focusing on the Adjacency Matrix. We first provide a calculation method that called node information entropy to update the matrix. Then, we analyze the search strategy in a continuous space and introduce the Deep Deterministic Policy Gradient (DDPG) method to overcome the demerit of the discrete space search. Finally, we integrate the GCN and reinforcement learning into an end-to-end framework. Our method can automatically define the adjacency matrix without artificial knowledge. At the same time, the proposed approach can deal with any size of the matrix and provide a better value for the network. Four popular datasets are selected to evaluate the capability of our algorithm. The method in this paper achieves the state-of-the-art performance on Cora and Pubmed datasets, respectively, with the accuracy of 84.6% and 81.6%.

Is the Meta-Learning Idea Able to Improve the Generalization of Deep Neural Networks on the Standard Supervised Learning?

Xiang Deng, Zhongfei Zhang

Responsive image

Auto-TLDR; Meta-learning Based Training of Deep Neural Networks for Few-Shot Learning

Slides Poster Similar

Substantial efforts have been made on improving the generalization abilities of deep neural networks (DNNs) in order to obtain better performances without introducing more parameters. On the other hand, meta-learning approaches exhibit powerful generalization on new tasks in few-shot learning. Intuitively, few-shot learning is more challenging than the standard supervised learning as each target class only has a very few or no training samples. The natural question that arises is whether the meta-learning idea can be used for improving the generalization of DNNs on the standard supervised learning. In this paper, we propose a novel meta-learning based training procedure (MLTP) for DNNs and demonstrate that the meta-learning idea can indeed improve the generalization abilities of DNNs. MLTP simulates the meta-training process by considering a batch of training samples as a task. The key idea is that the gradient descent step for improving the current task performance should also improve a new task performance, which is ignored by the current standard procedure for training neural networks. MLTP also benefits from all the existing training techniques such as dropout, weight decay, and batch normalization. We evaluate MLTP by training a variety of small and large neural networks on three benchmark datasets, i.e., CIFAR-10, CIFAR-100, and Tiny ImageNet. The experimental results show a consistently improved generalization performance on all the DNNs with different sizes, which verifies the promise of MLTP and demonstrates that the meta-learning idea is indeed able to improve the generalization of DNNs on the standard supervised learning.

Norm Loss: An Efficient yet Effective Regularization Method for Deep Neural Networks

Theodoros Georgiou, Sebastian Schmitt, Thomas Baeck, Wei Chen, Michael Lew

Responsive image

Auto-TLDR; Weight Soft-Regularization with Oblique Manifold for Convolutional Neural Network Training

Slides Poster Similar

Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to address these issues, researchers develop weight regularization methods and activation normalization methods. In this work we propose a weight soft-regularization method based on the Oblique manifold. The proposed method uses a loss function which pushes each weight vector to have a norm close to one, i.e. the weight matrix is smoothly steered toward the so-called Oblique manifold. We evaluate our method on the very popular CIFAR-10, CIFAR-100 and ImageNet 2012 datasets using two state-of-the-art architectures, namely the ResNet and wide-ResNet. Our method introduces negligible computational overhead and the results show that it is competitive to the state-of-the-art and in some cases superior to it. Additionally, the results are less sensitive to hyperparameter settings such as batch size and regularization factor.

PSDNet: A Balanced Architecture of Accuracy and Parameters for Semantic Segmentation

Yue Liu, Zhichao Lian

Responsive image

Auto-TLDR; Pyramid Pooling Module with SE1Cblock and D2SUpsample Network (PSDNet)

Slides Poster Similar

Abstract—In this paper, we present our Pyramid Pooling Module （PPM） with SE1Cblock and D2SUpsample Network (PSDNet), a novel architecture for accurate semantic segmentation. Started from the known work called Pyramid Scene Parsing Network (PSPNet), PSDNet takes advantage of pyramid pooling structure with channel attention module and feature transform module in Pyramid Pooling Module (PPM). The enhanced PPM with these two components can strengthen context information flowing in the network instead of damaging it. The channel attention module we mentioned is an improved “Squeeze and Excitation with 1D Convolution” (SE1C) block which can explicitly model interrelationship between channels with fewer number of parameters. We propose a feature transform module named “Depth to Space Upsampling” (D2SUpsample) in the PPM which keeps integrity of features by transforming features while interpolating features, at the same time reducing parameters. In addition, we introduce a joint strategy in SE1Cblock which combines two variants of global pooling without increasing parameters. Compared with PSPNet, our work achieves higher accuracy on public datasets with 73.97% mIoU and 82.89% mAcc accuracy on Cityscapes Dataset based on ResNet50 backbone.

LiNet: A Lightweight Network for Image Super Resolution

Armin Mehri, Parichehr Behjati Ardakani, Angel D. Sappa

Responsive image

Auto-TLDR; LiNet: A Compact Dense Network for Lightweight Super Resolution

Slides Poster Similar

This paper proposes a new lightweight network, LiNet, that enhancing technical efficiency in lightweight super resolution and operating approximately like very large and costly networks in terms of number of network parameters and operations. The proposed architecture allows the network to learn more abstract properties by avoiding low-level information via multiple links. LiNet introduces a Compact Dense Module, which contains set of inner and outer blocks, to efficiently extract meaningful information, to better leverage multi-level representations before upsampling stage, and to allow an efficient information and gradient flow within the network. Experiments on benchmark datasets show that the proposed LiNet achieves favorable performance against lightweight state-of-the-art methods.

CQNN: Convolutional Quadratic Neural Networks

Pranav Mantini, Shishir Shah

Responsive image

Auto-TLDR; Quadratic Neural Network for Image Classification

Slides Poster Similar

Image classification is a fundamental task in computer vision. A variety of deep learning models based on the Convolutional Neural Network (CNN) architecture have proven to be an efficient solution. Numerous improvements have been proposed over the years, where broader, deeper, and denser networks have been constructed. However, the atomic operation for these models has remained a linear unit (single neuron). In this work, we pursue an alternative dimension by hypothesizing the atomic operation to be performed by a quadratic unit. We construct convolutional layers using quadratic neurons for feature extraction and subsequently use dense layers for classification. We perform analysis to quantify the implication of replacing linear neurons with quadratic units. Results show a keen improvement in classification accuracy with quadratic neurons over linear neurons.

Slimming ResNet by Slimming Shortcut

Donggyu Joo, Doyeon Kim, Junmo Kim

Responsive image

Auto-TLDR; SSPruning: Slimming Shortcut Pruning on ResNet Based Networks

Slides Poster Similar

Conventional network pruning methods on convolutional neural networks (CNNs) reduce the number of input or output channels of convolution layers. With these approaches, the channels in the plain network can be pruned without any restrictions. However, in case of the ResNet based networks which have shortcuts (skip connections), the channel slimming of existing pruning methods is limited to the inside of each residual block. Since the number of Flops and parameters are also highly related to the number of channels in the shortcuts, more investigation on pruning channels in shortcuts is required. In this paper, we propose a novel pruning method, Slimming Shortcut Pruning (SSPruning), for pruning channels in shortcuts on ResNet based networks. First, we separate the long shortcut in individual regions that can be pruned independently without considering its long connections. Then, by applying our Importance Learning Gate (ILG) which learns the importance of channels globally regardless of channel type and location (i.e., in the shortcut or inside of the block), we can finally achieve an optimally pruned model. Through various experiments, we have confirmed that our method yields outstanding results when we prune the shortcuts and inside of the block together.

Learning Sparse Deep Neural Networks Using Efficient Structured Projections on Convex Constraints for Green AI

Michel Barlaud, Frederic Guyard

Responsive image

Auto-TLDR; Constrained Deep Neural Network with Constrained Splitting Projection

Slides Poster Similar

In recent years, deep neural networks (DNN) have been applied to different domains and achieved dramatic performance improvements over state-of-the-art classical methods. These performances of DNNs were however often obtained with networks containing millions of parameters and which training required heavy computational power. In order to cope with this computational issue a huge literature deals with proximal regularization methods which are time consuming.\\ In this paper, we propose instead a constrained approach. We provide the general framework for our new splitting projection gradient method. Our splitting algorithm iterates a gradient step and a projection on convex sets. We study algorithms for different constraints: the classical $\ell_1$ unstructured constraint and structured constraints such as the nuclear norm, the $\ell_{2,1} $ constraint (Group LASSO). We propose a new $\ell_{1,1} $ structured constraint for which we provide a new projection algorithm We demonstrate the effectiveness of our method on three popular datasets (MNIST, Fashion MNIST and CIFAR). Experiments on these datasets show that our splitting projection method with our new $\ell_{1,1} $ structured constraint provides the best reduction of memory and computational power. Experiments show that fully connected linear DNN are more efficient for green AI.

HFP: Hardware-Aware Filter Pruning for Deep Convolutional Neural Networks Acceleration

Fang Yu, Chuanqi Han, Pengcheng Wang, Ruoran Huang, Xi Huang, Li Cui

Responsive image

Auto-TLDR; Hardware-Aware Filter Pruning for Convolutional Neural Networks

Slides Poster Similar

Convolutional Neural Networks (CNNs) are powerful but computationally demanding and memory intensive, thus impeding their practical applications on resource-constrained hardware. Filter pruning is an efficient approach for deep CNN compression and acceleration, which aims to eliminate some filters with tolerable performance degradation. In the literature, the majority of approaches prune networks by defining the redundant filters or training the networks with a sparsity prior loss function. These approaches mainly use FLOPs as their speed metric. However, the inference latency of pruned networks cannot be directly controlled on the hardware platform, which is an important dimension of practicality. To address this issue, we propose a novel Hardware-aware Filter Pruning method (HFP) which can produce pruned networks that satisfy the actual latency budget on the hardwares of interest. In addition, we propose an iterative pruning framework called Opti-Cut to decrease the accuracy degradation of pruning process and accelerate the pruning procedure whilst meeting the hardware budget. More specifically, HFP first builds up a lookup table for fast estimating the latency of target network about filter configuration layer by layer. Then, HFP leverages information gain (IG) to globally evaluate the filters' contribution to network output distribution. HFP utilizes the Opti-Cut framework to globally prune filters with the minimum IG one by one until the latency budget is satisfied. We verify the effectiveness of the proposed method on CIFAR-10 and ImageNet. Compared with the state-of-the-art pruning methods, HFP demonstrates superior performances on VGGNet, ResNet and MobileNet V1/V2.

Attention As Activation

Yimian Dai, Stefan Oehmcke, Fabian Gieseke, Yiquan Wu, Kobus Barnard

Responsive image

Auto-TLDR; Attentional Activation Units for Convolutional Networks

Slides Similar

Activation functions and attention mechanisms are typically treated as having different purposes and have evolved differently. However, both concepts can be formulated as a non-linear gating function. Inspired by their similarity, we propose a novel type of activation units called attentional activation~(ATAC) units as a unification of activation functions and attention mechanisms. In particular, we propose a local channel attention module for the simultaneous non-linear activation and element-wise feature refinement, which locally aggregates point-wise cross-channel feature contexts. By replacing the well-known rectified linear units by such ATAC units in convolutional networks, we can construct fully attentional networks that perform significantly better with a modest number of additional parameters. We conducted detailed ablation studies on the ATAC units using several host networks with varying network depths to empirically verify the effectiveness and efficiency of the units. Furthermore, we compared the performance of the ATAC units against existing activation functions as well as other attention mechanisms on the CIFAR-10, CIFAR-100, and ImageNet datasets. Our experimental results show that networks constructed with the proposed ATAC units generally yield performance gains over their competitors given a comparable number of parameters.

Single Image Super-Resolution with Dynamic Residual Connection

Karam Park, Jae Woong Soh, Nam Ik Cho

Responsive image

Auto-TLDR; Dynamic Residual Attention Network for Lightweight Single Image Super-Residual Networks

Slides Poster Similar

Deep convolutional neural networks have shown significant improvement in the single image super-resolution (SISR) field. Recently, there have been attempts to solve the SISR problem using lightweight networks, considering limited computational resources for real-world applications. Especially for lightweight networks, balancing between parameter demand and performance is very difficult to adjust, and most lightweight SISR networks are manually designed based on a huge number of brute-force experiments. Besides, a critical key to the network performance relies on the skip connection of building blocks that are repeatedly in the architecture. Notably, in previous works, these connections are pre-defined and manually determined by human researchers. Hence, they are less flexible to the input image statistics, and there can be a better solution for the given number of parameters. Therefore, we focus on the automated design of networks regarding the connection of basic building blocks (residual networks), and as a result, propose a dynamic residual attention network (DRAN). The proposed method allows the network to dynamically select residual paths depending on the input image, based on the idea of attention mechanism. For this, we design a dynamic residual module that determines the residual paths between the basic building blocks for the given input image. By finding optimal residual paths between the blocks, the network can selectively bypass informative features needed to reconstruct the target high-resolution (HR) image. Experimental results show that our proposed DRAN outperforms most of the existing state-of-the-arts lightweight models in SISR.

P2 Net: Augmented Parallel-Pyramid Net for Attention Guided Pose Estimation

Luanxuan Hou, Jie Cao, Yuan Zhao, Haifeng Shen, Jian Tang, Ran He

Responsive image

Auto-TLDR; Parallel-Pyramid Net with Partial Attention for Human Pose Estimation

Slides Poster Similar

The target of human pose estimation is to determine the body parts and joint locations of persons in the image. Angular changes, motion blur and occlusion etc. in the natural scenes make this task challenging, while some joints are more difficult to be detected than others. In this paper, we propose an augmented Parallel-Pyramid Net (P^2Net) with an partial attention module. During data preprocessing, we proposed a differentiable auto data augmentation (DA^2) method in which sequences of data augmentations are formulated as a trainable and operational Convolution Neural Network (CNN) component. DA^2 improves the training efficiency and effectiveness. A parallel pyramid structure is followed to compensate the information loss introduced by the network. For the information loss problem in the backbone network, we optimize the backbone network by adopting a new parallel structure without increasing the overall computational complexity. To further refine the predictions after completion of global predictions, an Partial Attention Module (PAM) is defined to extract weighted features from different scale feature maps generated by the parallel pyramid structure. Compared with the traditional up-sampling refining, PAM can better capture the relationship between channels. Experiments corroborate the effectiveness of our proposed method. Notably, our method achieves the best performance on the challenging MSCOCO and MPII datasets.

Dynamic Multi-Path Neural Network

Yingcheng Su, Yichao Wu, Ken Chen, Ding Liang, Xiaolin Hu

Responsive image

Auto-TLDR; Dynamic Multi-path Neural Network

Slides Similar

Although deeper and larger neural networks have achieved better performance, due to overwhelming burden on computation, they cannot meet the demands of deployment on resource-limited devices. An effective strategy to address this problem is to make use of dynamic inference mechanism, which changes the inference path for different samples at runtime. Existing methods only reduce the depth by skipping an entire specific layer, which may lose important information in this layer. In this paper, we propose a novel method called Dynamic Multi-path Neural Network (DMNN), which provides more topology choices in terms of both width and depth on the fly. For better modelling the inference path selection, we further introduce previous state and object category information to guide the training process. Compared to previous dynamic inference techniques, the proposed method is more flexible and easier to incorporate into most modern network architectures. Experimental results on ImageNet and CIFAR-100 demonstrate the superiority of our method on both efficiency and classification accuracy.

Rethinking Experience Replay: A Bag of Tricks for Continual Learning

Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara

Responsive image

Auto-TLDR; Experience Replay for Continual Learning: A Practical Approach

Slides Poster Similar

In Continual Learning, a Neural Network is trained on a stream of data whose distribution shifts over time. Under these assumptions, it is especially challenging to improve on classes appearing later in the stream while remaining accurate on previous ones. This is due to the infamous problem of catastrophic forgetting, which causes a quick performance degradation when the classifier focuses on learning new categories. Recent literature proposed various approaches to tackle this issue, often resorting to very sophisticated techniques. In this work, we show that naive rehearsal can be patched to achieve similar performance. We point out some shortcomings that restrain Experience Replay (ER) and propose five tricks to mitigate them. Experiments show that ER, thus enhanced, displays an accuracy gain of 51.2 and 26.9 percentage points on the CIFAR-10 and CIFAR-100 datasets respectively (memory buffer size 1000). As a result, it surpasses current state-of-the-art rehearsal-based methods.

Contextual Classification Using Self-Supervised Auxiliary Models for Deep Neural Networks

Sebastian Palacio, Philipp Engler, Jörn Hees, Andreas Dengel

Responsive image

Auto-TLDR; Self-Supervised Autogenous Learning for Deep Neural Networks

Slides Poster Similar

Classification problems solved with deep neural networks (DNNs) typically rely on a closed world paradigm, and optimize over a single objective (e.g., minimization of the cross- entropy loss). This setup dismisses all kinds of supporting signals that can be used to reinforce the existence or absence of particular patterns. The increasing need for models that are interpretable by design makes the inclusion of said contextual signals a crucial necessity. To this end, we introduce the notion of Self-Supervised Autogenous Learning (SSAL). A SSAL objective is realized through one or more additional targets that are derived from the original supervised classification task, following architectural principles found in multi-task learning. SSAL branches impose low-level priors into the optimization process (e.g., grouping). The ability of using SSAL branches during inference, allow models to converge faster, focusing on a richer set of class-relevant features. We equip state-of-the-art DNNs with SSAL objectives and report consistent improvements for all of them on CIFAR100 and Imagenet. We show that SSAL models outperform similar state-of-the-art methods focused on contextual loss functions, auxiliary branches and hierarchical priors.

Progressive Gradient Pruning for Classification, Detection and Domain Adaptation

Le Thanh Nguyen-Meidine, Eric Granger, Marco Pedersoli, Madhu Kiran, Louis-Antoine Blais-Morin

Responsive image

Auto-TLDR; Progressive Gradient Pruning for Iterative Filter Pruning of Convolutional Neural Networks

Slides Poster Similar

Although deep neural networks (NNs) have achieved state-of-the-art accuracy in many visual recognition tasks, the growing computational complexity and energy consumption of networks remains an issue, especially for applications on plat-forms with limited resources and requiring real-time processing.Filter pruning techniques have recently shown promising results for the compression and acceleration of convolutional NNs(CNNs). However, these techniques involve numerous steps and complex optimisations because some only prune after training CNNs, while others prune from scratch during training by integrating sparsity constraints or modifying the loss function.In this paper we propose a new Progressive Gradient Pruning(PGP) technique for iterative filter pruning during training. In contrast to previous progressive pruning techniques, it relies on a novel filter selection criterion that measures the change in filter weights, uses a new hard and soft pruning strategy and effectively adapts momentum tensors during the backward propagation pass. Experimental results obtained after training various CNNs on image data for classification, object detection and domain adaptation benchmarks indicate that the PGP technique can achieve a better trade-off between classification accuracy and network (time and memory) complexity than PSFP and other state-of-the-art filter pruning techniques.

Exploiting Elasticity in Tensor Ranks for Compressing Neural Networks

Jie Ran, Rui Lin, Hayden Kwok-Hay So, Graziano Chesi, Ngai Wong

Responsive image

Auto-TLDR; Nuclear-Norm Rank Minimization Factorization for Deep Neural Networks

Slides Poster Similar

Elasticities in depth, width, kernel size and resolution have been explored in compressing deep neural networks (DNNs). Recognizing that the kernels in a convolutional neural network (CNN) are 4-way tensors, we further exploit a new elasticity dimension along the input-output channels. Specifically, a novel nuclear-norm rank minimization factorization (NRMF) approach is proposed to dynamically and globally search for the reduced tensor ranks during training. Correlation between tensor ranks across multiple layers is revealed, and a graceful tradeoff between model size and accuracy is obtained. Experiments then show the superiority of NRMF over the previous non-elastic variational Bayesian matrix factorization (VBMF) scheme.

A Systematic Investigation on Deep Architectures for Automatic Skin Lesions Classification

Pierluigi Carcagni, Marco Leo, Andrea Cuna, Giuseppe Celeste, Cosimo Distante

Responsive image

Auto-TLDR; RegNet: Deep Investigation of Convolutional Neural Networks for Automatic Classification of Skin Lesions

Slides Poster Similar

Computer vision-based techniques are more and more employed in healthcare and medical fields nowadays in order, principally, to be as a support to the experienced medical staff to help them to make a quick and correct diagnosis. One of the hot topics in this arena concerns the automatic classification of skin lesions. Several promising works exist about it, mainly leveraging Convolutional Neural Networks (CNN), but proposed pipeline mainly rely on complex data preprocessing and there is no systematic investigation about how available deep models can actually reach the accuracy needed for real applications. In order to overcome these drawbacks, in this work, an end-to-end pipeline is introduced and some of the most recent Convolutional Neural Networks (CNNs) architectures are included in it and compared on the largest common benchmark dataset recently introduced. To this aim, for the first time in this application context, a new network design paradigm, namely RegNet, has been exploited to get the best models among a population of configurations. The paper introduces a threefold level of contribution and novelty with respect the previous literature: the deep investigation of several CNN architectures driving to a consistent improvement of the lesions recognition accuracy, the exploitation of a new network design paradigm able to study the behavior of populations of models and a deep discussion about pro and cons of each analyzed method paving the path towards new research lines.

Revisiting the Training of Very Deep Neural Networks without Skip Connections

Oyebade Kayode Oyedotun, Abd El Rahman Shabayek, Djamila Aouada, Bjorn Ottersten

Responsive image

Auto-TLDR; Optimization of Very Deep PlainNets without shortcut connections with 'vanishing and exploding units' activations'

Slides Poster Similar

Deep neural networks (DNNs) with many layers of feature representations yield state-of-the-art results on several difficult learning tasks. However, optimizing very deep DNNs without shortcut connections known as PlainNets, is a notoriously hard problem. Considering the growing interest in this area, this paper investigates holistically two scenarios that plague the training of very deep PlainNets: (1) the relatively popular challenge of 'vanishing and exploding units' activations', and (2) the less investigated 'singularity' problem, which is studied in details in this paper. In contrast to earlier works that study only the saturation and explosion of units' activations in isolation, this paper harmonizes the inconspicuous coexistence of the aforementioned problems for very deep PlainNets. Particularly, we argue that the aforementioned problems would have to be tackled simultaneously for the successful training of very deep PlainNets. Finally, different techniques that can be employed for tackling the optimization problem are discussed, and a specific combination of simple techniques that allows the successful training of PlainNets having up to 100 layers is demonstrated.

Learning to Prune in Training via Dynamic Channel Propagation

Shibo Shen, Rongpeng Li, Zhifeng Zhao, Honggang Zhang, Yugeng Zhou

Responsive image

Auto-TLDR; Dynamic Channel Propagation for Neural Network Pruning

Slides Poster Similar

In this paper, we propose a novel network training mechanism called "dynamic channel propagation" to prune the model during the training period. In particular, we pick up a specific group of channels in each convolutional layer to participate in the forward propagation in training time according to the significance level of channel, which is defined as channel utility. The utility values with respect to all selected channels are updated simultaneously with the error back-propagation process and will constantly change. Furthermore, when the training ends, channels with high utility values are retained whereas those with low utility values are discarded. Hence, our proposed method trains and prunes neural networks simultaneously. We empirically evaluate our novel training method on various representative benchmark datasets and advanced convolutional neural network (CNN) architectures, including VGGNet and ResNet. The experiment results verify superior performance and robust effectiveness of our approach.

Transitional Asymmetric Non-Local Neural Networks for Real-World Dirt Road Segmentation

Yooseung Wang, Jihun Park

Responsive image

Auto-TLDR; Transitional Asymmetric Non-Local Neural Networks for Semantic Segmentation on Dirt Roads

Slides Poster Similar

Understanding images by predicting pixel-level semantic classes is a fundamental task in computer vision and is one of the most important techniques for autonomous driving. Recent approaches based on deep convolutional neural networks have dramatically improved the speed and accuracy of semantic segmentation on paved road datasets, however, dirt roads have yet to be systematically studied. Dirt roads do not contain clear boundaries between drivable and non-drivable regions; and thus, this difficulty must be overcome for the realization of fully autonomous vehicles. The key idea of our approach is to apply lightweight non-local blocks to reinforce stage-wise long-range dependencies in encoder-decoder style backbone networks. Experiments on 4,687 images of a dirt road dataset show that our transitional asymmetric non-local neural networks present a higher accuracy with lower computational costs compared to state-of-the-art models.

Second-Order Attention Guided Convolutional Activations for Visual Recognition

Shannan Chen, Qian Wang, Qiule Sun, Bin Liu, Jianxin Zhang, Qiang Zhang

Responsive image

Auto-TLDR; Second-order Attention Guided Network for Convolutional Neural Networks for Visual Recognition

Slides Poster Similar

Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.

Temporal Attention-Augmented Graph Convolutional Network for Efficient Skeleton-Based Human Action Recognition

Negar Heidari, Alexandros Iosifidis

Responsive image

Auto-TLDR; Temporal Attention Module for Efficient Graph Convolutional Network-based Action Recognition

Slides Poster Similar

Graph convolutional networks (GCNs) have been very successful in modeling non-Euclidean data structures, like sequences of body skeletons forming actions modeled as spatio-temporal graphs. Most GCN-based action recognition methods use deep feed-forward networks with high computational complexity to process all skeletons in an action. This leads to a high number of floating point operations (ranging from 16G to 100G FLOPs) to process a single sample, making their adoption in restricted computation application scenarios infeasible. In this paper, we propose a temporal attention module (TAM) for increasing the efficiency in skeleton-based action recognition by selecting the most informative skeletons of an action at the early layers of the network. We incorporate the TAM in a light-weight GCN topology to further reduce the overall number of computations. Experimental results on two benchmark datasets show that the proposed method outperforms with a large margin the baseline GCN-based method while having 2.9 times less number of computations. Moreover, it performs on par with the state-of-the-art with up to 9.6 times less number of computations.

A Systematic Investigation on End-To-End Deep Recognition of Grocery Products in the Wild

Marco Leo, Pierluigi Carcagni, Cosimo Distante

Responsive image

Auto-TLDR; Automatic Recognition of Products on grocery shelf images using Convolutional Neural Networks

Slides Poster Similar

Automatic recognition of products on grocery shelf images is a new and attractive topic in computer vision and machine learning since, it can be exploited in different application areas. This paper introduces a complete end-to-end pipeline (without preliminary radiometric and spatial transformations usually involved while dealing with the considered issue) and it provides a systematic investigation of recent machine learning models based on convolutional neural networks for addressing the product recognition task by exploiting the proposed pipeline on a recent challenging grocery product dataset. The investigated models were never been used in this context: they derive from the successful and more generic object recognition task and have been properly tuned to address this specific issue. Besides, also ensembles of nets built by most advanced theoretical fundaments have been taken into account. Gathered classification results were very encouraging since the recognition accuracy has been improved up to 15\% with respect to the leading approaches in the state of art on the same dataset. A discussion about the pros and cons of the investigated solutions are discussed by paving the path towards new research lines.

Filter Pruning Using Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks

Kakeru Mitsuno, Takio Kurita

Responsive image

Auto-TLDR; Hierarchical Group Sparse Regularization for Sparse Convolutional Neural Networks

Slides Poster Similar

Since the convolutional neural networks are often trained with redundant parameters, it is possible to reduce redundant kernels or filters to obtain a compact network without dropping the classification accuracy. In this paper, we propose a filter pruning method using the hierarchical group sparse regularization. It is shown in our previous work that the hierarchical group sparse regularization is effective in obtaining sparse networks in which filters connected to unnecessary channels are automatically close to zero. After training the convolutional neural network with the hierarchical group sparse regularization, the unnecessary filters are selected based on the increase of the classification loss of the randomly selected training samples to obtain a compact network. It is shown that the proposed method can reduce more than 50% parameters of ResNet for CIFAR-10 with only 0.3% decrease in the accuracy of test samples. Also, 34% parameters of ResNet are reduced for TinyImageNet-200 with higher accuracy than the baseline network.

Context-Aware Residual Module for Image Classification

Jing Bai, Ran Chen

Responsive image

Auto-TLDR; Context-Aware Residual Module for Image Classification

Slides Poster Similar

Attention module has achieved great success in numerous vision tasks. However, existing visual attention modules generally consider the features of a single-scale, and cannot make full use of their multi-scale contextual information. Meanwhile, the multi-scale spatial feature representation has demonstrated its outstanding performance in a wide range of applications. However, the multi-scale features are always represented in a layer-wise manner, i.e. it is impossible to know their contextual information at a granular level. Focusing on the above issue, a context-aware residual module for image classification is proposed in this paper. It consists of a novel multi-scale channel attention module MSCAM to learn refined channel weights by considering the visual features of its own scale and its surrounding fields, and a multi-scale spatial aware module MSSAM to further capture more spatial information. Either or both of the two modules can be plugged into any CNN-based backbone image classification architecture with a short residual connection to obtain the context-aware enhanced features. The experiments on public image recognition datasets including CIFAR10, CIFAR100，Tiny-ImageNet and ImageNet consistently demonstrate that our proposed modules significantly outperforms a wide-used state-of-the-art methods, e.g., ResNet and the lightweight networks of MobileNet and SqueezeeNet.

On the Information of Feature Maps and Pruning of Deep Neural Networks

Mohammadreza Soltani, Suya Wu, Jie Ding, Robert Ravier, Vahid Tarokh

Responsive image

Auto-TLDR; Compressing Deep Neural Models Using Mutual Information

Slides Poster Similar

A technique for compressing deep neural models achieving competitive performance to state-of-the-art methods is proposed. The approach utilizes the mutual information between the feature maps and the output of the model in order to prune the redundant layers of the network. Extensive numerical experiments on both CIFAR-10, CIFAR-100, and Tiny ImageNet data sets demonstrate that the proposed method can be effective in compressing deep models, both in terms of the numbers of parameters and operations. For instance, by applying the proposed approach to DenseNet model with 0.77 million parameters and 293 million operations for classification of CIFAR-10 data set, a reduction of 62.66% and 41.00% in the number of parameters and the number of operations are respectively achieved, while increasing the test error only by less than 1%.

ResNet-Like Architecture with Low Hardware Requirements

Elena Limonova, Daniil Alfonso, Dmitry Nikolaev, Vladimir V. Arlazarov

Responsive image

Auto-TLDR; BM-ResNet: Bipolar Morphological ResNet for Image Classification

Slides Poster Similar

One of the most computationally intensive parts in modern recognition systems is an inference of deep neural networks that are used for image classification, segmentation, enhancement, and recognition. The growing popularity of edge computing makes us look for ways to reduce its time for mobile and embedded devices. One way to decrease the neural network inference time is to modify a neuron model to make it more efficient for computations on a specific device. The example of such a model is a bipolar morphological neuron model. The bipolar morphological neuron is based on the idea of replacing multiplication with addition and maximum operations. This model has been demonstrated for simple image classification with LeNet-like architectures [1]. In the paper, we introduce a bipolar morphological ResNet (BM-ResNet) model obtained from a much more complex ResNet architecture by converting its layers to bipolar morphological ones. We apply BM-ResNet to image classification on MNIST and CIFAR-10 datasets with only a moderate accuracy decrease from 99.3% to 99.1% and from 85.3% to 85.1%. We also estimate the computational complexity of the resulting model. We show that for the majority of ResNet layers, the considered model requires 2.1-2.9 times fewer logic gates for implementation and 15-30% lower latency.

Enhancing Semantic Segmentation of Aerial Images with Inhibitory Neurons

Ihsan Ullah, Sean Reilly, Michael Madden

Responsive image

Auto-TLDR; Lateral Inhibition in Deep Neural Networks for Object Recognition and Semantic Segmentation

Slides Poster Similar

In a Convolutional Neural Network, each neuron in the output feature map takes input from the neurons in its receptive field. This receptive field concept plays a vital role in today's deep neural networks. However, inspired by neuro-biological research, it has been proposed to add inhibitory neurons outside the receptive field, which may enhance the performance of neural network models. In this paper, we begin with deep network architectures such as VGG and ResNet, and propose an approach to add lateral inhibition in each output neuron to reduce its impact on its neighbours, both in fine-tuning pre-trained models and training from scratch. Our experiments show that notable improvements upon prior baseline deep models can be achieved. A key feature of our approach is that it is easy to add to baseline models; it can be adopted in any model containing convolution layers, and we demonstrate its value in applications including object recognition and semantic segmentation of aerial images, where we show state-of-the-art result on the Aeroscape dataset. On semantic segmentation tasks, our enhancement shows 17.43% higher mIoU than a single baseline model on a single source (the Aeroscape dataset), 13.43% higher performance than an ensemble model on the same single source, and 7.03% higher than an ensemble model on multiple sources (segmentation datasets). Our experiments illustrate the potential impact of using inhibitory neurons in deep learning models, and they also show better results than the baseline models that have standard convolutional layer.

MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values

Claudio Filipi Gonçalves Santos, Danilo Colombo, Mateus Roder, Joao Paulo Papa

Responsive image

Auto-TLDR; MaxDropout: A Regularizer for Deep Neural Networks

Slides Poster Similar

Different techniques have emerged in the deep learning scenario, such as Convolutional Neural Networks, Deep Belief Networks, and Long Short-Term Memory Networks, to cite a few. In lockstep, regularization methods, which aim to prevent overfitting by penalizing the weight connections, or turning off some units, have been widely studied either. In this paper, we present a novel approach called MaxDropout, a regularizer for deep neural network models that works in a supervised fashion by removing (shutting off) the prominent neurons (i.e., most active) in each hidden layer. The model forces fewer activated units to learn more representative information, thus providing sparsity. Regarding the experiments, we show that it is possible to improve existing neural networks and provide better results in neural networks when Dropout is replaced by MaxDropout. The proposed method was evaluated in image classification, achieving comparable results to existing regularizers, such as Cutout and RandomErasing, also improving the accuracy of neural networks that uses Dropout by replacing the existing layer by MaxDropout.

Efficient Super Resolution by Recursive Aggregation

Zhengxiong Luo Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan

Responsive image

Auto-TLDR; Recursive Aggregation Network for Efficient Deep Super Resolution

Slides Poster Similar

Deep neural networks have achieved remarkable results on image super resolution (SR), but the efficiency problem of deep SR networks is rarely studied. We experimentally find that many sequentially stacked convolutional blocks in nowadays SR networks are far from being fully optimized, which largely damages their overall efficiency. It indicates that comparable or even better results could be achieved with less but sufficiently optimized blocks. In this paper, we try to construct more efficient SR model via the proposed recursive aggregation network (RAN). It recursively aggregates convolutional blocks in different orders, and avoids too many sequentially stacked blocks. In this way, multiple shortcuts are introduced in RAN, and help gradients easier flow to all inner layers, even for very deep SR networks. As a result, all blocks in RAN can be better optimized, thus RAN can achieve better performance with smaller model size than existing methods.

Attention Pyramid Module for Scene Recognition

Zhinan Qiao, Xiaohui Yuan, Chengyuan Zhuang, Abolfazl Meyarian

Responsive image

Auto-TLDR; Attention Pyramid Module for Multi-Scale Scene Recognition

Slides Poster Similar

The unrestricted open vocabulary and diverse substances of scenery images bring significant challenges to scene recognition. However, most deep learning architectures and attention methods are developed on general-purpose datasets and omit the characteristics of scene data. In this paper, we exploit the attention pyramid module (APM) to tackle the predicament of scene recognition. Our method streamlines the multi-scale scene recognition pipeline, learns comprehensive scene features at various scales and locations, addresses the interdependency among scales, and further assists feature re-calibration as well as aggregation process. APM is extremely light-weighted and can be easily plugged into existing network architectures in a parameter-efficient manner. By simply integrating APM into ResNet-50, we obtain a 3.54\% boost in terms of top-1 accuracy on the benchmark scene dataset. Comprehensive experiments show that APM achieves better performance comparing with state-of-the-art attention methods using significant less computation budget. Code and pre-trained models will be made publicly available.

Feature-Dependent Cross-Connections in Multi-Path Neural Networks

Dumindu Tissera, Kasun Vithanage, Rukshan Wijesinghe, Kumara Kahatapitiya, Subha Fernando, Ranga Rodrigo

Responsive image

Auto-TLDR; Multi-path Networks for Adaptive Feature Extraction

Slides Poster Similar

Learning a particular task from a dataset, samples in which originate from diverse contexts, is challenging, and usually addressed by deepening or widening standard neural networks. As opposed to conventional network widening, multi-path architectures restrict the quadratic increment of complexity to a linear scale. However, existing multi-column/path networks or model ensembling methods do not consider any feature-dependant allocation of parallel resources, and therefore, tend to learn redundant features. Given a layer in a multi-path network, if we restrict each path to learn a context-specific set of features and introduce a mechanism to intelligently allocate incoming feature maps to such paths, each path can specialize in a certain context, reducing the redundancy and improving the quality of extracted features. This eventually leads to better-optimized usage of parallel resources. To do this, we propose inserting feature-dependant cross-connections between parallel sets of feature maps in successive layers. The weights of these cross-connections are learned based on the input features of the particular layer. Our multi-path networks show improved image recognition accuracy at a similar complexity compared to conventional and state-of-the-art methods for deepening, widening and adaptive feature extracting, in both small and large scale datasets.

Can Data Placement Be Effective for Neural Networks Classification Tasks? Introducing the Orthogonal Loss

Brais Cancela, Veronica Bolon-Canedo, Amparo Alonso-Betanzos

Responsive image

Auto-TLDR; Spatial Placement for Neural Network Training Loss Functions

Slides Poster Similar

Traditionally, a Neural Network classification training loss function follows the same principle: minimizing the distance between samples that belong to the same class, while maximizing the distance to the other classes. There are no restrictions on the spatial placement of deep features (last layer input). This paper addresses this issue when dealing with Neural Networks, providing a set of loss functions that are able to train a classifier by forcing the deep features to be projected over a predefined orthogonal basis. Experimental results shows that these `data placement' functions can overcome the training accuracy provided by the classic cross-entropy loss function.