ICPR2020 Paper Browser

Paper download is intended for registered attendees only, and is subjected to the IEEE Copyright Policy. Any other use is strongly forbidden.

A New Convex Loss Function for Multiple Instance Support Vector Machines

Sang-Baeg Kim, Jung-Man Bae

Auto-TLDR; WR-SVM: A Novel Multiple Instance SVM for Video Classification

Abstract Slides Poster

We develop a novel multiple instance SVM based on maximizing the minimum witness rate (WR) among positive bags. We solve this nonlinear integer programming problem by the multiple instance SVM formulation with a convex loss function, where unknown integer labels of instances in positive bags are relaxed to real numbers between -1 and 1 using the tanh(.) to estimate WR of a positive bag. Our new model, WR-SVM, also can incorporates the multiple instance learning (MIL) problem into a simple deep neural network framework with no additional MIL pooling layers. WR-SVM is robust to input perturbation by eliminating the imbalance between positive instances allocated to positive bags. A better generalization ability is expected by the large margin due to the balanced allocation of positive instances to positive bags. We perform experiments on various video datasets to verify the effectiveness of our method for video classification. The results of WR-SVM outperform the state-of-the-art for MIL-based video classification models.

Similar papers

Sparse Network Inversion for Key Instance Detection in Multiple Instance Learning

Beomjo Shin, Junsu Cho, Hwanjo Yu, Seungjin Choi

Auto-TLDR; Improving Attention-based Deep Multiple Instance Learning for Key Instance Detection (KID)

Abstract Slides Poster Similar

Multiple Instance Learning (MIL) involves predicting a single label for a bag of instances, given positive or negative labels at bag-level, without accessing to label for each instance in the training phase. Since a positive bag contains both positive and negative instances, it is often required to detect positive instances (key instances) when a set of instances is categorized as a positive bag. The attention-based deep MIL model is a recent advance in both bag-level classification and key instance detection (KID). However, if the positive and negative instances in a positive bag are not clearly distinguishable, the attention-based deep MIL model has limited KID performance as the attention scores are skewed to few positive instances. In this paper, we present a method to improve the attention-based deep MIL model in the task of KID. The main idea is to use the neural network inversion to find which instances made contribution to the bag-level prediction produced by the trained MIL model. Moreover, we incorporate a sparseness constraint into the neural network inversion, leading to the sparse network inversion which is solved by the proximal gradient method. Numerical experiments on an MNIST-based image MIL dataset and two real-world histopathology datasets verify the validity of our method, demonstrating the KID performance is significantly improved while the performance of bag-level prediction is maintained.

Low-Cost Lipschitz-Independent Adaptive Importance Sampling of Stochastic Gradients

Huikang Liu, Xiaolu Wang, Jiajin Li, Man-Cho Anthony So

Auto-TLDR; Adaptive Importance Sampling for Stochastic Gradient Descent

Abstract Slides Similar

Stochastic gradient descent (SGD) usually samples training data based on the uniform distribution, which may not be a good choice because of the high variance of its stochastic gradient. Thus, importance sampling methods are considered in the literature to improve the performance. Most previous work on SGD-based methods with importance sampling requires the knowledge of Lipschitz constants of all component gradients, which are in general difficult to estimate. In this paper, we study an adaptive importance sampling method for common SGD-based methods by exploiting the local first-order information without knowing any Lipschitz constants. In particular, we periodically changes the sampling distribution by only utilizing the gradient norms in the past few iterations. We prove that our adaptive importance sampling non-asymptotically reduces the variance of the stochastic gradients in SGD, and thus better convergence bounds than that for vanilla SGD can be obtained. We extend this sampling method to several other widely used stochastic gradient algorithms including SGD with momentum and ADAM. Experiments on common convex learning problems and deep neural networks illustrate notably enhanced performance using the adaptive sampling strategy.

An Efficient Empirical Solver for Localized Multiple Kernel Learning Via DNNs

Ziming Zhang

Auto-TLDR; Localized Multiple Kernel Learning using LMKL-Net

Abstract Slides Poster Similar

In this paper we propose solving localized multiple kernel learning (LMKL) using LMKL-Net, a feedforward deep neural network (DNN). In contrast to previous works, as a learning principle we propose parameterizing the gating function for learning kernel combination weights and the multiclass classifier using an attentional network (AN) and a multilayer perceptron (MLP), respectively. Such interpretability helps us better understand how the network solves the problem. Thanks to stochastic gradient descent (SGD), our approach has {\em linear} computational complexity in training. Empirically on benchmark datasets we demonstrate that with comparable or better accuracy than the state-of-the-art, our LMKL-Net can be trained about {\bf two orders of magnitude} faster with about {\bf two orders of magnitude} smaller memory footprint for large-scale learning.

Soft Label and Discriminant Embedding Estimation for Semi-Supervised Classification

Fadi Dornaika, Abdullah Baradaaji, Youssof El Traboulsi

Auto-TLDR; Semi-supervised Semi-Supervised Learning for Linear Feature Extraction and Label Propagation

Abstract Slides Poster Similar

In recent times, graph-based semi-supervised learning proved to be a powerful paradigm for processing and mining large datasets. The main advantage relies on the fact that these methods can be useful in propagating a small set of known labels to a large set of unlabeled data. The scarcity of labeled data may affect the performance of the semi-learning. This paper introduces a new semi-supervised framework for simultaneous linear feature extraction and label propagation. The proposed method simultaneously estimates a discriminant transformation and the unknown label by exploiting both labeled and unlabeled data. In addition, the unknowns of the learning model are estimated by integrating two types of graph-based smoothness constraints. The resulting semi-supervised model is expected to learn more discriminative information. Experiments are conducted on six public image datasets. These experimental results show that the performance of the proposed method can be better than that of many state-of-the-art graph-based semi-supervised algorithms.

Learning Sign-Constrained Support Vector Machines

Kenya Tajima, Kouhei Tsuchida, Esmeraldo Ronnie Rey Zara, Naoya Ohta, Tsuyoshi Kato

Auto-TLDR; Constrained Sign Constraints for Learning Linear Support Vector Machine

Abstract Poster Similar

Domain knowledge is useful to improve the generalization performance of learning machines. Sign constraints are a handy representation to combine domain knowledge with learning machine. In this paper, we consider constraining the signs of the weight coefficients in learning the linear support vector machine, and develop two optimization algorithms for minimizing the empirical risk under the sign constraints. One of the two algorithms is based on the projected gradient method, in which each iteration of the projected gradient method takes O(nd) computational cost and the sublinear convergence of the objective error is guaranteed. The second algorithm is based on the Frank-Wolfe method that also converges sublinearly and possesses a clear termination criterion. We show that each iteration of the Frank-Wolfe also requires O(nd) cost. Furthermore, we derive the explicit expression for the minimal iteration number to ensure an epsilon-accurate solution by analyzing the curvature of the objective function. Finally, we empirically demonstrate that the sign constraints are a promising technique when similarities to the training examples compose the feature vector.

Hierarchical Multimodal Attention for Deep Video Summarization

Melissa Sanabria, Frederic Precioso, Thomas Menguy

Auto-TLDR; Automatic Summarization of Professional Soccer Matches Using Event-Stream Data and Multi- Instance Learning

Abstract Slides Poster Similar

The way people consume sports on TV has drastically evolved in the last years, particularly under the combined effects of the legalization of sport betting and the huge increase of sport analytics. Several companies are nowadays sending observers in the stadiums to collect live data of all the events happening on the field during the match. Those data contain meaningful information providing a very detailed description of all the actions occurring during the match to feed the coaches and staff, the fans, the viewers, and the gamblers. Exploiting all these data, sport broadcasters want to generate extra content such as match highlights, match summaries, players and teams analytics, etc., to appeal subscribers. This paper explores the problem of summarizing professional soccer matches as automatically as possible using both the aforementioned event-stream data collected from the field and the content broadcasted on TV. We have designed an architecture, introducing first (1) a Multiple Instance Learning method that takes into account the sequential dependency among events and then (2) a hierarchical multimodal attention layer that grasps the importance of each event in an action. We evaluate our approach on matches from two professional European soccer leagues, showing its capability to identify the best actions for automatic summarization by comparing with real summaries made by human operators.

Feature Extraction and Selection Via Robust Discriminant Analysis and Class Sparsity

Ahmad Khoder, Fadi Dornaika

Auto-TLDR; Hybrid Linear Discriminant Embedding for supervised multi-class classification

Abstract Slides Poster Similar

The main goal of discriminant embedding is to extract features that can be compact and informative representations of the original set of features. This paper introduces a hybrid scheme for linear feature extraction for supervised multi-class classification. We introduce a unifying criterion that is able to retain the advantages of robust sparse LDA and Inter-class sparsity. Thus, the estimated transformation includes two types of discrimination which are the inter-class sparsity and robust Linear Discriminant Analysis with feature selection. In order to optimize the proposed objective function, we deploy an iterative alternating minimization scheme for estimating the linear transformation and the orthogonal matrix. The introduced scheme is generic in the sense that it can be used for combining and tuning many other linear embedding methods. In the lights of the experiments conducted on six image datasets including faces, objects, and digits, the proposed scheme was able to outperform competing methods in most of the cases.

Unveiling Groups of Related Tasks in Multi-Task Learning

Jordan Frecon, Saverio Salzo, Massimiliano Pontil

Auto-TLDR; Continuous Bilevel Optimization for Multi-Task Learning

Abstract Slides Poster Similar

A common approach in multi-task learning is to encourage the tasks to share a low dimensional representation. This has led to the popular method of trace norm regularization, which has proved effective in many applications. In this paper, we extend this approach by allowing the tasks to partition into different groups, within which trace norm regularization is separately applied. We propose a continuous bilevel optimization framework to simultaneously identify groups of related tasks and learn a low dimensional representation within each group. Hinging on recent results on the derivative of generalized matrix functions, we devise a smooth approximation of the upper-level objective via a dual forward-backward algorithm with Bregman distances. This allows us to solve the bilevel problem by a gradient-based scheme. Numerical experiments on synthetic and benchmark datasets support the effectiveness of the proposed method.

Attention Based Multi-Instance Thyroid Cytopathological Diagnosis with Multi-Scale Feature Fusion

Shuhao Qiu, Yao Guo, Chuang Zhu, Wenli Zhou, Huang Chen

Auto-TLDR; A weakly supervised multi-instance learning framework based on attention mechanism with multi-scale feature fusion for thyroid cytopathological diagnosis

Abstract Slides Poster Similar

In recent years, deep learning has been popular in combining with cytopathology diagnosis. Using the whole slide images (WSI) scanned by electronic scanners at clinics, researchers have developed many algorithms to classify the slide (benign or malignant). However, the key area that support the diagnosis result can be relatively small in a thyroid WSI, and only the global label can be acquired, which make the direct use of the strongly supervised learning framework infeasible. What’s more, because the clinical diagnosis of the thyroid cells requires the use of visual features in different scales, a generic feature extraction way may not achieve good performance. In this paper, we propose a weakly supervised multi-instance learning framework based on attention mechanism with multi-scale feature fusion (MSF) using convolutional neural network (CNN) for thyroid cytopathological diagnosis. We take each WSI as a bag, each bag contains multiple instances which are the different regions of the WSI, our framework is trained to learn the key area automatically and make the classification. We also propose a feature fusion structure, merge the low-level features into the final feature map and add an instance-level attention module in it, which improves the classification accuracy. Our model is trained and tested on the collected clinical data, reaches the accuracy of 93.2%, which outperforms the other existing methods. We also tested our model on a public histopathology dataset and achieves better result than the state-of-the-art deep multi-instance method.

On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks

Wolfgang Roth, Günther Schindler, Holger Fröning, Franz Pernkopf

Auto-TLDR; Quantization-Aware Bayesian Network Classifiers for Small-Scale Scenarios

Abstract Slides Poster Similar

We present two methods to reduce the complexity of Bayesian network (BN) classifiers. First, we introduce quantization-aware training using the straight-through gradient estimator to quantize the parameters of BNs to few bits. Second, we extend a recently proposed differentiable tree-augmented naive Bayes (TAN) structure learning approach to also consider the model size. Both methods are motivated by recent developments in the deep learning community, and they provide effective means to trade off between model size and prediction accuracy, which is demonstrated in extensive experiments. Furthermore, we contrast quantized BN classifiers with quantized deep neural networks (DNNs) for small-scale scenarios which have hardly been investigated in the literature. We show Pareto optimal models with respect to model size, number of operations, and test error and find that both model classes are viable options.

Adversarial Encoder-Multi-Task-Decoder for Multi-Stage Processes

Andre Mendes, Julian Togelius, Leandro Dos Santos Coelho

Auto-TLDR; Multi-Task Learning and Semi-Supervised Learning for Multi-Stage Processes

Abstract Similar

In multi-stage processes, decisions occur in an ordered sequence of stages. Early stages usually have more observations with general information (easier/cheaper to collect), while later stages have fewer observations but more specific data. This situation can be represented by a dual funnel structure, in which the sample size decreases from one stage to the other while the information increases. Training classifiers in this scenario is challenging since information in the early stages may not contain distinct patterns to learn (underfitting). In contrast, the small sample size in later stages can cause overfitting. We address both cases by introducing a framework that combines adversarial autoencoders (AAE), multi-task learning (MTL), and multi-label semi-supervised learning (MLSSL). We improve the decoder of the AAE with an MTL component so it can jointly reconstruct the original input and use feature nets to predict the features for the next stages. We also introduce a sequence constraint in the output of an MLSSL classifier to guarantee the sequential pattern in the predictions. Using real-world data from different domains (selection process, medical diagnosis), we show that our approach outperforms other state-of-the-art methods.

Deep Multiple Instance Learning with Spatial Attention for ROP Case Classification, Instance Selection and Abnormality Localization

Xirong Li, Wencui Wan, Yang Zhou, Jianchun Zhao, Qijie Wei, Junbo Rong, Pengyi Zhou, Limin Xu, Lijuan Lang, Yuying Liu, Chengzhi Niu, Dayong Ding, Xuemin Jin

Auto-TLDR; MIL-SA: Deep Multiple Instance Learning for Automated Screening of Retinopathy of Prematurity

Abstract Similar

This paper tackles automated screening of Retinopathy of Prematurity (ROP), one of the most common causes of visual loss in childhood. Clinically, ROP screening per case requires multiple color fundus images capturing different zones of the premature retina. A desirable model shall not only make a decision at the case level, but also pinpoint which instances and what part of the instances are responsible for the decision. This paper makes the first attempt to accomplish three tasks, i.e, ROP case classification, instance selection and abnormality localization in a unified framework. To that end, we propose a new model that effectively combines instance-attention based deep multiple instance learning (MIL) and spatial attention (SA). The propose model, which we term MIL-SA, identifies positive instances in light of their contributions to case-level decision. Meanwhile, abnormal regions in the identified instances are automatically localized by the SA mechanism. Moreover, MIL-SA is learned from case-level binary labels exclusively, and in an end-to-end manner. Experiments on a large clinical dataset of 2,186 cases with 11,053 fundus images show the viability of the proposed model for all the three tasks.

Meta Soft Label Generation for Noisy Labels

Görkem Algan, Ilkay Ulusoy

Auto-TLDR; MSLG: Meta-Learning for Noisy Label Generation

Abstract Slides Poster Similar

The existence of noisy labels in the dataset causes significant performance degradation for deep neural networks (DNNs). To address this problem, we propose a Meta Soft Label Generation algorithm called MSLG, which can jointly generate soft labels using meta-learning techniques and learn DNN parameters in an end-to-end fashion. Our approach adapts the meta-learning paradigm to estimate optimal label distribution by checking gradient directions on both noisy training data and noise-free meta-data. In order to iteratively update soft labels, meta-gradient descent step is performed on estimated labels, which would minimize the loss of noise-free meta samples. In each iteration, the base classifier is trained on estimated meta labels. MSLG is model-agnostic and can be added on top of any existing model at hand with ease. We performed extensive experiments on CIFAR10, Clothing1M and Food101N datasets. Results show that our approach outperforms other state-of-the-art methods by a large margin. Our code is available at \url{https://github.com/gorkemalgan/MSLG_noisy_label}.

DAIL: Dataset-Aware and Invariant Learning for Face Recognition

Gaoang Wang, Chen Lin, Tianqiang Liu, Mingwei He, Jiebo Luo

Auto-TLDR; DAIL: Dataset-Aware and Invariant Learning for Face Recognition

Abstract Slides Poster Similar

To achieve good performance in face recognition, a large scale training dataset is usually required. A simple yet effective way for improving the recognition performance is to use a dataset as large as possible by combining multiple datasets in the training. However, it is problematic and troublesome to naively combine different datasets due to two major issues. Firstly, the same person can possibly appear in different datasets, leading to the identity overlapping issue between different datasets. Natively treating the same person as different classes in different datasets during training will affect back-propagation and generate non-representative embeddings. On the other hand, manually cleaning labels will take a lot of human efforts, especially when there are millions of images and thousands of identities. Secondly, different datasets are collected in different situations and thus will lead to different domain distributions. Natively combining datasets will lead to domain distribution differences and make it difficult to learn domain invariant embeddings across different datasets. In this paper, we propose DAIL: Dataset-Aware and Invariant Learning to resolve the above-mentioned issues. To solve the first issue of identity overlapping, we propose a dataset-aware loss for multi-dataset training by reducing the penalty when the same person appears in multiple datasets. This can be readily achieved with a modified softmax loss with a dataset-aware term. To solve the second issue, the domain adaptation with gradient reversal layers is employed for dataset invariant learning. The proposed approach not only achieves state-of-the-art results on several commonly used face recognition validation sets, like LFW, CFP-FP, AgeDB-30, but also shows great benefit for practical usage.

Malware Detection by Exploiting Deep Learning over Binary Programs

Panpan Qi, Zhaoqi Zhang, Wei Wang, Chang Yao

Auto-TLDR; End-to-End Malware Detection without Feature Engineering

Abstract Slides Poster Similar

Malware evolves rapidly over time, which makes existing solutions being ineffective in detecting newly released malware. Machine learning models that can learn to capture malicious patterns directly from the data play an increasingly important role in malware analysis. However, traditional machine learning models heavily depend on feature engineering. The extracted static features are vulnerable as hackers could create new malware with different feature values to deceive the machine learning models. In this paper, we propose an end-to-end malware detection framework consisting of convolutional neural network, autoencoder and neural decision trees. It learns the features from multiple domains for malware detection without feature engineering. In addition, since anti-virus products should have a very low false alarm rate to avoid annoying users, we propose a special loss function, which optimizes the recall for a fixed low false positive rate (e.g., less than 0.1%). Experiments show that the proposed framework has achieved a better recall than the baseline models, and the derived loss function also makes a difference.

Feature Extraction by Joint Robust Discriminant Analysis and Inter-Class Sparsity

Fadi Dornaika, Ahmad Khoder

Auto-TLDR; Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA_FSIS)

Abstract Slides Similar

Feature extraction methods have been successfully applied to many real-world applications. The classical Linear Discriminant Analysis (LDA) and its variants are widely used as feature extraction methods. Although they have been used for different classification tasks, these methods have some shortcomings. The main one is that the projection axes obtained are not informative about the relevance of original features. In this paper, we propose a linear embedding method that merges two interesting properties: Robust LDA and inter-class sparsity. Furthermore, the targeted projection transformation focuses on the most discriminant original features. The proposed method is called Robust Discriminant Analysis with Feature Selection and Inter-class Sparsity (RDA_FSIS). Two kinds of sparsity are explicitly included in the proposed model. The first kind is obtained by imposing the $\ell_{2,1}$ constraint on the projection matrix in order to perform feature ranking. The second kind is obtained by imposing the inter-class sparsity constraint used for getting a common sparsity structure in each class. Comprehensive experiments on five real-world image datasets demonstrate the effectiveness and advantages of our framework over existing linear methods.

Meta Generalized Network for Few-Shot Classification

Wei Wu, Shanmin Pang, Zhiqiang Tian, Yaochen Li

Auto-TLDR; Meta Generalized Network for Few-Shot Classification

Abstract Similar

Few-shot classification aims to learn a well performance model with very limited labeled examples. There are mainly two directions for this aim, namely, meta- and metric-learning. Meta learning trains models in a particular way to fast adapt to new tasks, but it neglects variational features of images. Metric learning considers relationships among same or different classes, however on the downside, it usually fails to achieve competitive performance on unseen boundary examples. In this paper, we propose a Meta Generalized Network (MGNet) that aims to combine advantages of both meta- and metric-learning. There are two novel components in MGNet. Specifically, we first develop a meta backbone training method that both learns a flexible feature extractor and a classifier initializer efficiently, delightedly leading to fast adaption to unseen few-shot tasks without overfitting. Second, we design a trainable adaptive interval model to improve the cosine classifier, which increases the recognition accuracy of hard examples. We train the meta backbone in the training stage by all classes, and fine-tune the meta-backbone as well as train the adaptive classifier in the testing stage.

Generalization Comparison of Deep Neural Networks Via Output Sensitivity

Mahsa Forouzesh, Farnood Salehi, Patrick Thiran

Auto-TLDR; Generalization of Deep Neural Networks using Sensitivity

Abstract Slides Similar

Although recent works have brought some insights into the performance improvement of techniques used in state-of-the-art deep-learning models, more work is needed to understand their generalization properties. We shed light on this matter by linking the loss function to the output's sensitivity to its input. We find a rather strong empirical relation between the output sensitivity and the variance in the bias-variance decomposition of the loss function, which hints on using sensitivity as a metric for comparing the generalization performance of networks, without requiring labeled data. We find that sensitivity is decreased by applying popular methods which improve the generalization performance of the model, such as (1) using a deep network rather than a wide one, (2) adding convolutional layers to baseline classifiers instead of adding fully-connected layers, (3) using batch normalization, dropout and max-pooling, and (4) applying parameter initialization techniques.

MetaMix: Improved Meta-Learning with Interpolation-based Consistency Regularization

Yangbin Chen, Yun Ma, Tom Ko, Jianping Wang, Qing Li

Auto-TLDR; MetaMix: A Meta-Agnostic Meta-Learning Algorithm for Few-Shot Classification

Abstract Slides Poster Similar

Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot classification methods. They train an initializer across a variety of sampled learning tasks (also known as episodes) such that the initialized model can adapt quickly to new tasks. However, within each episode, current MAML-based algorithms have limitations in forming generalizable decision boundaries using only a few training examples. In this paper, we propose an approach called MetaMix. It generates virtual examples within each episode to regularize the backbone models. MetaMix can be applied in any of the MAML-based algorithms and learn the decision boundaries which are more generalizable to new tasks. Experiments on the mini-ImageNet, CUB, and FC100 datasets show that MetaMix improves the performance of MAML-based algorithms and achieves the state-of-the-art result when applied in Meta-Transfer Learning.

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

Michel Barlaud, Frederic Guyard

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

Abstract 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.

Learning Emotional Blinded Face Representations

Alejandro Peña Almansa, Julian Fierrez, Agata Lapedriza, Aythami Morales

Auto-TLDR; Blind Face Representations for Emotion Recognition

Abstract Slides Poster Similar

This work proposes two new face representations that are blind to the expressions associated to emotional responses. This work is in part motivated by new international regulations for personal data protection, which force data controllers to protect any kind of sensitive information involved in automatic processes. The advances in affective computing have contributed to improve human-machine interfaces, but at the same time, the capacity to monitorize emotional responses trigger potential risks for humans, both in terms of fairness and privacy. We propose two different methods to learn these facial expression blinded features. We show that it is possible to eliminate information related to emotion recognition tasks, while the performance of subject verification, gender recognition, and ethnicity classification are just slightly affected. We also present an application to train fairer classifiers over a protected facial expression attribute. The results demonstrate that it is possible to reduce emotional information in the face representation while retaining competitive performance in other face-based artificial intelligence tasks.

A Unified Framework for Distance-Aware Domain Adaptation

Fei Wang, Youdong Ding, Huan Liang, Yuzhen Gao, Wenqi Che

Auto-TLDR; distance-aware domain adaptation

Abstract Slides Poster Similar

Unsupervised domain adaptation has achieved significant results by leveraging knowledge from a source domain to learn a related but unlabeled target domain. Previous methods are insufficient to model domain discrepancy and class discrepancy, which may lead to misalignment and poor adaptation performance. To address this problem, in this paper, we propose a unified framework, called distance-aware domain adaptation, which is fully aware of both cross-domain distance and class-discriminative distance. In addition, second-order statistics distance and manifold alignment are also exploited to extract more information from data. In this manner, the generalization error of the target domain in classification problems can be reduced substantially. To validate the proposed method, we conducted experiments on five public datasets and an ablation study. The results demonstrate the good performance of our proposed method.

Cam-Softmax for Discriminative Deep Feature Learning

Tamas Suveges, Stephen James Mckenna

Auto-TLDR; Cam-Softmax: A Generalisation of Activations and Softmax for Deep Feature Spaces

Abstract Slides Poster Similar

Deep convolutional neural networks are widely used to learn feature spaces for image classification tasks. We propose cam-softmax, a generalisation of the final layer activations and softmax function, that encourages deep feature spaces to exhibit high intra-class compactness and high inter-class separability. We provide an algorithm to automatically adapt the method's main hyperparameter so that it gradually diverges from the standard activations and softmax method during training. We report experiments using CASIA-Webface, LFW, and YTF face datasets demonstrating that cam-softmax leads to representations well suited to open-set face recognition and face pair matching. Furthermore, we provide empirical evidence that cam-softmax provides some robustness to class labelling errors in training data, making it of potential use for deep learning from large datasets with poorly verified labels.

Few-Shot Few-Shot Learning and the Role of Spatial Attention

Yann Lifchitz, Yannis Avrithis, Sylvaine Picard

Auto-TLDR; Few-shot Learning with Pre-trained Classifier on Large-Scale Datasets

Abstract Slides Poster Similar

Few-shot learning is often motivated by the ability of humans to learn new tasks from few examples. However, standard few-shot classification benchmarks assume that the representation is learned on a limited amount of base class data, ignoring the amount of prior knowledge that a human may have accumulated before learning new tasks. At the same time, even if a powerful representation is available, it may happen in some domain that base class data are limited or non-existent. This motivates us to study a problem where the representation is obtained from a classifier pre-trained on a large-scale dataset of a different domain, assuming no access to its training process, while the base class data are limited to few examples per class and their role is to adapt the representation to the domain at hand rather than learn from scratch. We adapt the representation in two stages, namely on the few base class data if available and on the even fewer data of new tasks. In doing so, we obtain from the pre-trained classifier a spatial attention map that allows focusing on objects and suppressing background clutter. This is important in the new problem, because when base class data are few, the network cannot learn where to focus implicitly. We also show that a pre-trained network may be easily adapted to novel classes, without meta-learning.

Supervised Domain Adaptation Using Graph Embedding

Lukas Hedegaard, Omar Ali Sheikh-Omar, Alexandros Iosifidis

Auto-TLDR; Domain Adaptation from the Perspective of Multi-view Graph Embedding and Dimensionality Reduction

Abstract Slides Poster Similar

Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of multi-view graph embedding and dimensionality reduction. Instead of solving the generalised eigenvalue problem to perform the embedding, we formulate the graph-preserving criterion as loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework which generalises the prior methods CCSA and d-SNE, and enables simple and effective loss designs; an LDA-inspired instantiation of the framework leads to performance on par with the state-of-the-art on the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.

Semi-Supervised Class Incremental Learning

Alexis Lechat, Stéphane Herbin, Frederic Jurie

Auto-TLDR; incremental class learning with non-annotated batches

Abstract Slides Poster Similar

This paper makes a contribution to the problem of incremental class learning, the principle of which is to sequentially introduce batches of samples annotated with new classes during the learning phase. The main objective is to reduce the drop in classification performance on old classes, a phenomenon commonly called catastrophic forgetting. We propose in this paper a new method which exploits the availability of a large quantity of non-annotated images in addition to the annotated batches. These images are used to regularize the classifier and give the feature space a more stable structure. We demonstrate on several image data sets that our approach is able to improve the global performance of classifiers learned using an incremental learning protocol, even with annotated batches of small size.

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

Sebastian Palacio, Philipp Engler, Jörn Hees, Andreas Dengel

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

Abstract 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.

Watermelon: A Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy

Xiang Xie, Wilhelm Stork

Auto-TLDR; Feature Selection Using Bayes Error Rate Estimation for Dynamic Feature Selection

Abstract Slides Poster Similar

Feature selection has become a crucial part of many classification problems in which high-dimensional datasets may contain tens of thousands of features. In this paper, we propose a novel feature selection method scoring the features through estimating the Bayes error rate based on kernel density estimation. Additionally, we update the scores of features dynamically by quantitatively interpreting the effects of feature relevance and redundancy in a new way. Distinguishing from the common heuristic applied by many feature selection methods, which prefers choosing features that are not relevant to each other, our approach penalizes only monotonically correlated features and rewards any other kind of relevance among features based on Spearman’s rank correlation coefficient and normalized mutual information. We conduct extensive experiments on seventeen diverse classification benchmarks, the results show that our approach overperforms other seventeen popular state-of-the-art feature selection methods in most cases.

Dual-Mode Iterative Denoiser: Tackling the Weak Label for Anomaly Detection

Shuheng Lin, Hua Yang

Auto-TLDR; A Dual-Mode Iterative Denoiser for Crowd Anomaly Detection

Abstract Slides Poster Similar

Crowd anomaly detection suffers from limited training data under weak supervision. In this paper, we propose a dual-mode iterative denoiser to tackle the weak label challenge for anomaly detection. First, we use a convolution autoencoder (CAE) in image space to act as a cluster for grouping similar video clips, where the spatial-temporal similarity helps the cluster metric to represent the reconstruction error. Then we use the graph convolution neural network (GCN) to explore the temporal correlation and the feature similarity between video clips within different rough labels, where the classifier can be constantly updated in the label denoising process. Without specific image-level labels, our model can predict the clip-level anomaly probabilities for videos. Extensive experiment results on two public datasets show that our approach performs favorably against the state-of-the-art methods.

Deep Gait Relative Attribute Using a Signed Quadratic Contrastive Loss

Yuta Hayashi, Shehata Allam, Yasushi Makihara, Daigo Muramatsu, Yasushi Yagi

Auto-TLDR; Signal-Contrastive Loss for Gait Attributes Estimation

Abstract Similar

This paper presents a deep learning-based method to estimate gait attributes (e.g., stately, cool, relax, etc.). Similarly to the existing studies on relative attribute, human perception-based annotations on the gait attributes are given to pairs of gait videos (i.e., the first one is better, tie, and the second one is better), and the relative annotations are utilized to train a ranking model of the gait attribute. More specifically, we design a Siamese (i.e., two-stream) network which takes a pair of gait inputs and output gait attribute score for each. We then introduce a suitable loss function called a signed contrastive loss to train the network parameters with the relative annotation. Unlike the existing loss functions for learning to rank does not inherent a nice property of a quadratic contrastive loss, the proposed signed quadratic contrastive loss function inherents the nice property. The quantitative evaluation results reveal that the proposed method shows better or comparable accuracies of relative attribute prediction against the baseline methods.

Local Clustering with Mean Teacher for Semi-Supervised Learning

Zexi Chen, Benjamin Dutton, Bharathkumar Ramachandra, Tianfu Wu, Ranga Raju Vatsavai

Auto-TLDR; Local Clustering for Semi-supervised Learning

Abstract Slides Similar

The Mean Teacher (MT) model of Tarvainen and Valpola has shown favorable performance on several semi-supervised benchmark datasets. MT maintains a teacher model's weights as the exponential moving average of a student model's weights and minimizes the divergence between their probability predictions under diverse perturbations of the inputs. However, MT is known to suffer from confirmation bias, that is, reinforcing incorrect teacher model predictions. In this work, we propose a simple yet effective method called Local Clustering (LC) to mitigate the effect of confirmation bias. In MT, each data point is considered independent of other points during training; however, data points are likely to be close to each other in feature space if they share similar features. Motivated by this, we cluster data points locally by minimizing the pairwise distance between neighboring data points in feature space. Combined with a standard classification cross-entropy objective on labeled data points, the misclassified unlabeled data points are pulled towards high-density regions of their correct class with the help of their neighbors, thus improving model performance. We demonstrate on semi-supervised benchmark datasets SVHN and CIFAR-10 that adding our LC loss to MT yields significant improvements compared to MT and performance comparable to the state of the art in semi-supervised learning.

RNN Training along Locally Optimal Trajectories via Frank-Wolfe Algorithm

Yun Yue, Ming Li, Venkatesh Saligrama, Ziming Zhang

Auto-TLDR; Frank-Wolfe Algorithm for Efficient Training of RNNs

Abstract Slides Poster Similar

We propose a novel and efficient training method for RNNs by iteratively seeking a local minima on the loss surface within a small region, and leverage this directional vector for the update, in an outer-loop. We propose to utilize the Frank-Wolfe (FW) algorithm in this context. Although, FW implicitly involves normalized gradients, which can lead to a slow convergence rate, we develop a novel RNN training method that, surprisingly, even with the additional cost, the overall training cost is empirically observed to be lower than back-propagation. Our method leads to a new Frank-Wolfe method, that is in essence an SGD algorithm with a restart scheme. We prove that under certain conditions our algorithm has a sublinear convergence rate of $O(1/\epsilon)$ for $\epsilon$ error. We then conduct empirical experiments on several benchmark datasets including those that exhibit long-term dependencies, and show significant performance improvement. We also experiment with deep RNN architectures and show efficient training performance. Finally, we demonstrate that our training method is robust to noisy data.

Rank-Based Ordinal Classification

Joan Serrat, Idoia Ruiz

Auto-TLDR; Ordinal Classification with Order

Abstract Slides Poster Similar

Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class {\em labels}. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1\ldots $C$, being $C$ the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to less likely. This is tanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, \textit{i.e.}, most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset.

Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification

Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Doulamis, Nikolaos Bakalos

Auto-TLDR; Tensor-Based Neural Network for Spatiotemporal Pose Classifiaction using Three-Dimensional Skeleton Data

Abstract Slides Poster Similar

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classifiaction using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.

Revisiting ImprovedGAN with Metric Learning for Semi-Supervised Learning

Jaewoo Park, Yoon Gyo Jung, Andrew Teoh

Auto-TLDR; Improving ImprovedGAN with Metric Learning for Semi-supervised Learning

Abstract Slides Poster Similar

Semi-supervised Learning (SSL) is a classical problem where a model needs to solve classification as it is trained on a partially labeled train data. After the introduction of generative adversarial network (GAN) and its success, the model has been modified to be applicable to SSL. ImprovedGAN as a representative model for GAN-based SSL, it showed promising performance on the SSL problem. However, the inner mechanism of this model has been only partially revealed. In this work, we revisit ImprovedGAN with a fresh perspective based on metric learning. In particular, we interpret ImprovedGAN by general pair weighting, a recent framework in metric learning. Based on this interpretation, we derive two theoretical properties of ImprovedGAN: (i) its discriminator learns to make confident predictions over real samples, (ii) the adversarial interaction in ImprovedGAN along with semi-supervision results in cluster separation by reducing intra-class variance and increasing the inter-class variance, thereby improving the model generalization. These theoretical implications are experimentally supported. Motivated by the findings, we propose a variant of ImprovedGAN, called Intensified ImprovedGAN (I2GAN), where its cluster separation characteristic is enhanced by two proposed techniques: (a) the unsupervised discriminator loss is scaled up and (b) the generated batch size is enlarged. As a result, I2GAN produces better class-wise cluster separation and, hence, generalization. Extensive experiments on the widely known benchmark data sets verify the effectiveness of our proposed method, showing that its performance is better than or comparable to other GAN based SSL models.

Memetic Evolution of Training Sets with Adaptive Radial Basis Kernels for Support Vector Machines

Jakub Nalepa, Wojciech Dudzik, Michal Kawulok

Auto-TLDR; Memetic Algorithm for Evolving Support Vector Machines with Adaptive Kernels

Abstract Slides Poster Similar

Support vector machines (SVMs) are a supervised learning technique that can be applied in both binary and multi-class classification and regression tasks. SVMs seamlessly handle continuous and categorical variables. Their training is, however, both time- and memory-costly for large training data, and selecting an incorrect kernel function or its hyperparameters leads to suboptimal decision hyperplanes. In this paper, we introduce a memetic algorithm for evolving SVM training sets with adaptive radial basis function kernels to not only make the deployment of SVMs easier for emerging big data applications, but also to improve their generalization abilities over the unseen data. We build upon two observations: first, only a small subset of all training vectors, called the support vectors, contribute to the position of the decision boundary, hence the other vectors can be removed from the training set without deteriorating the performance of the model. Second, selecting different kernel hyperparameters for different training vectors may help better reflect the subtle characteristics of the space while determining the hyperplane. The experiments over almost 100 benchmark and synthetic sets showed that our algorithm delivers models outperforming both SVMs optimized using state-of-the-art evolutionary techniques, and other supervised learners.

Inferring Tasks and Fluents in Videos by Learning Causal Relations

Haowen Tang, Ping Wei, Huan Li, Nanning Zheng

Auto-TLDR; Joint Learning of Complex Task and Fluent States in Videos

Abstract Slides Poster Similar

Recognizing time-varying object states in complex tasks is an important and challenging issue. In this paper, we propose a novel model to jointly infer object fluents and complex tasks in videos. A task is a complex goal-driven human activity and a fluent is defined as a time-varying object state. A hierarchical graph represents a task as a human action stream and multiple concurrent object fluents which vary as the human performs the actions. In this process, the human actions serve as the causes of object state changes which conversely reflect the effects of human actions. Given an input video, a causal sampling beam search (CSBS) algorithm is proposed to jointly infer the task category and the states of objects in each video frame. For model learning, a structural SVM framework is adopted to jointly train the task, fluent, cause, and effect parameters. We collected a new large-scale dataset of tasks and fluents in third-person view videos. It contains 14 categories of tasks, 24 categories of object fluents, 50 categories of object states, 809 videos, and 333,351 frames. Experimental results demonstrate the effectiveness of the proposed method.

Hcore-Init: Neural Network Initialization Based on Graph Degeneracy

Stratis Limnios, George Dasoulas, Dimitrios Thilikos, Michalis Vazirgiannis

Auto-TLDR; K-hypercore: Graph Mining for Deep Neural Networks

Abstract Slides Poster Similar

Neural networks are the pinnacle of Artificial Intelligence, as in recent years we witnessed many novel architectures, learning and optimization techniques for deep learning. Capitalizing on the fact that neural networks inherently constitute multipartite graphs among neuron layers, we aim to analyze directly their structure to extract meaningful information that can improve the learning process. To our knowledge graph mining techniques for enhancing learning in neural networks have not been thoroughly investigated. In this paper we propose an adapted version of the k-core structure for the complete weighted multipartite graph extracted from a deep learning architecture. As a multipartite graph is a combination of bipartite graphs, that are in turn the incidence graphs of hypergraphs, we design k-hypercore decomposition, the hypergraph analogue of k-core degeneracy. We applied k-hypercore to several neural network architectures, more specifically to convolutional neural networks and multilayer perceptrons for image recognition tasks after a very short pretraining. Then we used the information provided by the hypercore numbers of the neurons to re-initialize the weights of the neural network, thus biasing the gradient optimization scheme. Extensive experiments proved that k-hypercore outperforms the state-of-the-art initialization methods.

Variational Deep Embedding Clustering by Augmented Mutual Information Maximization

Qiang Ji, Yanfeng Sun, Yongli Hu, Baocai Yin

Auto-TLDR; Clustering by Augmented Mutual Information maximization for Deep Embedding

Abstract Slides Poster Similar

Clustering is a crucial but challenging task in pattern analysis and machine learning. Recent many deep clustering methods combining representation learning with cluster techniques emerged. These deep clustering methods mainly focus on the correlation among samples and ignore the relationship between samples and their representations. In this paper, we propose a novel end-to-end clustering framework, namely variational deep embedding clustering by augmented mutual information maximization (VCAMI). From the perspective of VAE, we prove that minimizing reconstruction loss is equivalent to maximizing the mutual information of the input and its latent representation. This provides a theoretical guarantee for us to directly maximize the mutual information instead of minimizing reconstruction loss. Therefore we proposed the augmented mutual information which highlights the uniqueness of the representations while discovering invariant information among similar samples. Extensive experiments on several challenging image datasets show that the VCAMI achieves good performance. we achieve state-of-the-art results for clustering on MNIST (99.5%) and CIFAR-10 (65.4%) to the best of our knowledge.

Multi-Label Contrastive Focal Loss for Pedestrian Attribute Recognition

Xiaoqiang Zheng, Zhenxia Yu, Lin Chen, Fan Zhu, Shilong Wang

Auto-TLDR; Multi-label Contrastive Focal Loss for Pedestrian Attribute Recognition

Abstract Slides Poster Similar

Pedestrian Attribute Recognition (PAR) has received extensive attention during the past few years. With the advances of deep constitutional neural networks (CNNs), the performance of PAR has been significantly improved. Existing methods tend to acquire attribute-specific features by designing various complex network structures with additional modules. Such additional modules, however, dramatically increase the number of parameters. Meanwhile, the problems of class imbalance and hard attribute retrieving remain underestimated in PAR. In this paper, we explore the optimization mechanism of the training processing to account for these problems and propose a new loss function called Multi-label Contrastive Focal Loss (MCFL). This proposed MCFL emphasizes the hard and minority attributes by using a separated re-weighting mechanism for different positive and negative classes to alleviate the impact of the imbalance. MCFL is also able to enlarge the gaps between the intra-class of multi-label attributes, to force CNNs to extract more subtle discriminative features. We evaluate the proposed MCFL on three large public pedestrian datasets, including RAP, PA-100K, and PETA. The experimental results indicate that the proposed MCFL with the ResNet-50 backbone is able to outperform other state-of-the-art approaches in comparison.

Learning to Rank for Active Learning: A Listwise Approach

Minghan Li, Xialei Liu, Joost Van De Weijer, Bogdan Raducanu

Auto-TLDR; Learning Loss for Active Learning

Abstract Slides Similar

Active learning emerged as an alternative to alleviate the effort to label huge amount of data for data-hungry applications (such as image/video indexing and retrieval, autonomous driving, etc.). The goal of active learning is to automatically select a number of unlabeled samples for annotation (according to a budget), based on an acquisition function, which indicates how valuable a sample is for training the model. The learning loss method is a task-agnostic approach which attaches a module to learn to predict the target loss of unlabeled data, and select data with the highest loss for labeling. In this work, we follow this strategy but we define the acquisition function as a learning to rank problem and rethink the structure of the loss prediction module, using a simple but effective listwise approach. Experimental results on four datasets demonstrate that our method outperforms recent state-of-the-art active learning approaches for both image classification and regression tasks.

Quasibinary Classifier for Images with Zero and Multiple Labels

Liao Shuai, Efstratios Gavves, Changyong Oh, Cees Snoek

Auto-TLDR; Quasibinary Classifiers for Zero-label and Multi-label Classification

Abstract Slides Poster Similar

The softmax and binary classifier are commonly preferred for image classification applications. However, as softmax is specifically designed for categorical classification, it assumes each image has just one class label. This limits its applicability for problems where the number of labels does not equal one, most notably zero- and multi-label problems. In these challenging settings, binary classifiers are, in theory, better suited. However, as they ignore the correlation between classes, they are not as accurate and scalable in practice. In this paper, we start from the observation that the only difference between binary and softmax classifiers is their normalization function. Specifically, while the binary classifier self-normalizes its score, the softmax classifier combines the scores from all classes before normalization. On the basis of this observation we introduce a normalization function that is learnable, constant, and shared between classes and data points. By doing so, we arrive at a new type of binary classifier that we coin quasibinary classifier. We show in a variety of image classification settings, and on several datasets, that quasibinary classifiers are considerably better in classification settings where regular binary and softmax classifiers suffer, including zero-label and multi-label classification. What is more, we show that quasibinary classifiers yield well-calibrated probabilities allowing for direct and reliable comparisons, not only between classes but also between data points.

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

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

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

Abstract 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.

Generative Deep-Neural-Network Mixture Modeling with Semi-Supervised MinMax+EM Learning

Nilay Pande, Suyash Awate

Auto-TLDR; Semi-supervised Deep Neural Networks for Generative Mixture Modeling and Clustering

Abstract Slides Poster Similar

Deep neural networks (DNNs) for generative mixture modeling typically rely on unsupervised learning that employs hard clustering schemes, or variational learning with loose / approximate bounds, or under-regularized modeling. We propose a novel statistical framework for a DNN mixture model using a single generative adversarial network. Our learning formulation proposes a novel data-likelihood term relying on a well-regularized / constrained Gaussian mixture model in the latent space along with a prior term on the DNN weights. Our min-max learning increases the data likelihood using a tight variational lower bound using expectation maximization (EM). We leverage our min-max EM learning scheme for semi-supervised learning. Results on three real-world datasets demonstrate the benefits of our compact modeling and learning formulation over the state of the art for mixture modeling and clustering.

Augmented Bi-Path Network for Few-Shot Learning

Baoming Yan, Chen Zhou, Bo Zhao, Kan Guo, Yang Jiang, Xiaobo Li, Zhang Ming, Yizhou Wang

Auto-TLDR; Augmented Bi-path Network for Few-shot Learning

Abstract Slides Poster Similar

Few-shot Learning (FSL) which aims to learn from few labeled training data is becoming a popular research topic, due to the expensive labeling cost in many real-world applications. One kind of successful FSL method learns to compare the testing (query) image and training (support) image by simply concatenating the features of two images and feeding it into the neural network. However, with few labeled data in each class, the neural network has difficulty in learning or comparing the local features of two images. Such simple image-level comparison may cause serious mis-classification. To solve this problem, we propose Augmented Bi-path Network (ABNet) for learning to compare both global and local features on multi-scales. Specifically, the salient patches are extracted and embedded as the local features for every image. Then, the model learns to augment the features for better robustness. Finally, the model learns to compare global and local features separately, \emph{i.e.}, in two paths, before merging the similarities. Extensive experiments show that the proposed ABNet outperforms the state-of-the-art methods. Both quantitative and visual ablation studies are provided to verify that the proposed modules lead to more precise comparison results.

Teacher-Student Training and Triplet Loss for Facial Expression Recognition under Occlusion

Mariana-Iuliana Georgescu, Radu Ionescu

Auto-TLDR; Knowledge Distillation for Facial Expression Recognition under Occlusion

Abstract Slides Similar

In this paper, we study the task of facial expression recognition under strong occlusion. We are particularly interested in cases where 50% of the face is occluded, e.g. when the subject wears a Virtual Reality (VR) headset. While previous studies show that pre-training convolutional neural networks (CNNs) on fully-visible (non-occluded) faces improves the accuracy, we propose to employ knowledge distillation to achieve further improvements. First of all, we employ the classic teacher-student training strategy, in which the teacher is a CNN trained on fully-visible faces and the student is a CNN trained on occluded faces. Second of all, we propose a new approach for knowledge distillation based on triplet loss. During training, the goal is to reduce the distance between an anchor embedding, produced by a student CNN that takes occluded faces as input, and a positive embedding (from the same class as the anchor), produced by a teacher CNN trained on fully-visible faces, so that it becomes smaller than the distance between the anchor and a negative embedding (from a different class than the anchor), produced by the student CNN. Third of all, we propose to combine the distilled embeddings obtained through the classic teacher-student strategy and our novel teacher-student strategy based on triplet loss into a single embedding vector. We conduct experiments on two benchmarks, FER+ and AffectNet, with two CNN architectures, VGG-f and VGG-face, showing that knowledge distillation can bring significant improvements over the state-of-the-art methods designed for occluded faces in the VR setting. Furthermore, we obtain accuracy rates that are quite close to the state-of-the-art models that take as input fully-visible faces. For example, on the FER+ data set, our VGG-face based on concatenated distilled embeddings attains an accuracy rate of 82.75% on lower-half-visible faces, which is only 2.24% below the accuracy rate of a state-of-the-art VGG-13 that is evaluated on fully-visible faces. Given that our model sees only the lower-half of the face, we consider this to be a remarkable achievement. In conclusion, we consider that our distilled CNN models can provide useful feedback for the task of recognizing the facial expressions of a person wearing a VR headset.

Class Conditional Alignment for Partial Domain Adaptation

Mohsen Kheirandishfard, Fariba Zohrizadeh, Farhad Kamangar

Auto-TLDR; Multi-class Adversarial Adaptation for Partial Domain Adaptation

Abstract Slides Poster Similar

Adversarial adaptation models have demonstrated significant progress towards transferring knowledge from a labeled source dataset to an unlabeled target dataset. Partial domain adaptation (PDA) investigates the scenarios in which the source domain is large and diverse, and the target label space is a subset of the source label space. The main purpose of PDA is to identify the shared classes between the domains and promote learning transferable knowledge from these classes. In this paper, we propose a multi-class adversarial architecture for PDA. The proposed approach jointly aligns the marginal and class-conditional distributions in the shared label space by minimaxing a novel multi-class adversarial loss function. Furthermore, we incorporate effective regularization terms to encourage selecting the most relevant subset of source domain classes. In the absence of target labels, the proposed approach is able to effectively learn domain-invariant feature representations, which in turn can enhance the classification performance in the target domain. Comprehensive experiments on three benchmark datasets Office-$31$, Office-Home, and Caltech-Office corroborate the effectiveness of the proposed approach in addressing different partial transfer learning tasks.

Rethinking Deep Active Learning: Using Unlabeled Data at Model Training

Oriane Siméoni, Mateusz Budnik, Yannis Avrithis, Guillaume Gravier

Auto-TLDR; Unlabeled Data for Active Learning

Abstract Slides Poster Similar

Active learning typically focuses on training a model on few labeled examples alone, while unlabeled ones are only used for acquisition. In this work we depart from this setting by using both labeled and unlabeled data during model training across active learning cycles. We do so by using unsupervised feature learning at the beginning of the active learning pipeline and semi-supervised learning at every active learning cycle, on all available data. The former has not been investigated before in active learning, while the study of latter in the context of deep learning is scarce and recent findings are not conclusive with respect to its benefit. Our idea is orthogonal to acquisition strategies by using more data, much like ensemble methods use more models. By systematically evaluating on a number of popular acquisition strategies and datasets, we find that the use of unlabeled data during model training brings a spectacular accuracy improvement in image classification, compared to the differences between acquisition strategies. We thus explore smaller label budgets, even one label per class.