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Pseudo Rehearsal Using Non Photo-Realistic Images

Bhasker Sri Harsha Suri, Kalidas Yeturu

Auto-TLDR; Pseudo-Rehearsing for Catastrophic Forgetting

Abstract Slides Poster

Deep Neural networks forget previously learnt tasks when they are faced with learning new tasks. This is called catastrophic forgetting. Rehearsing the neural network with the training data of the previous task can protect the network from catastrophic forgetting.Since rehearsing requires the storage of entire previous data, Pseudo rehearsal was proposed, where samples belonging to the previous data are generated synthetically for rehearsal. In an image classification setting, while current techniques try to generate synthetic data that is photo-realistic, we demonstrated that Neural networks can be rehearsed on data that is not photo-realistic and still achieve good retention of the previous task. We also demonstrated that forgoing the constraint of having photo realism in the generated data can result in a significant reduction in the consumption of computational and memory resources for pseudo rehearsal.

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Class-Incremental Learning with Pre-Allocated Fixed Classifiers

Federico Pernici, Matteo Bruni, Claudio Baecchi, Francesco Turchini, Alberto Del Bimbo

Auto-TLDR; Class-Incremental Learning with Pre-allocated Output Nodes for Fixed Classifier

Abstract Slides Poster Similar

In class-incremental learning, a learning agent faces a stream of data with the goal of learning new classes while not forgetting previous ones. Neural networks are known to suffer under this setting, as they forget previously acquired knowledge. To address this problem, effective methods exploit past data stored in an episodic memory while expanding the final classifier nodes to accommodate the new classes. In this work, we substitute the expanding classifier with a novel fixed classifier in which a number of pre-allocated output nodes are subject to the classification loss right from the beginning of the learning phase. Contrarily to the standard expanding classifier, this allows: (a) the output nodes of future unseen classes to firstly see negative samples since the beginning of learning together with the positive samples that incrementally arrive; (b) to learn features that do not change their geometric configuration as novel classes are incorporated in the learning model. Experiments with public datasets show that the proposed approach is as effective as the expanding classifier while exhibiting intriguing properties of internal feature representation that are otherwise not-existent. Our ablation study on pre-allocating a large number of classes further validates the approach.

Rethinking Experience Replay: A Bag of Tricks for Continual Learning

Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara

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

Abstract 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.

Augmentation of Small Training Data Using GANs for Enhancing the Performance of Image Classification

Shih-Kai Hung, John Q. Gan

Auto-TLDR; Generative Adversarial Network for Image Training Data Augmentation

Abstract Slides Poster Similar

It is difficult to achieve high performance without sufficient training data for deep convolutional neural networks (DCNNs) to learn. Data augmentation plays an important role in improving robustness and preventing overfitting in machine learning for many applications such as image classification. In this paper, a novel method for data augmentation is proposed to solve the problem of machine learning with small training datasets. The proposed method can synthesise similar images with rich diversity from only a single original training sample to increase the number of training data by using generative adversarial networks (GANs). It is expected that the synthesised images possess class-informative features, which may be in the validation or testing data but not in the training data due to that the training dataset is small, and thus they can be effective as augmented training data to improve classification accuracy of DCNNs. The experimental results have demonstrated that the proposed method with a novel GAN framework for image training data augmentation can significantly enhance the classification performance of DCNNs for applications where original training data is limited.

RSAC: Regularized Subspace Approximation Classifier for Lightweight Continuous Learning

Chih-Hsing Ho, Shang-Ho Tsai

Auto-TLDR; Regularized Subspace Approximation Classifier for Lightweight Continuous Learning

Abstract Slides Poster Similar

Continuous learning seeks to perform the learning on the data that arrives from time to time. While prior works have demonstrated several possible solutions, these approaches require excessive training time as well as memory usage. This is impractical for applications where time and storage are constrained, such as edge computing. In this work, a novel training algorithm, regularized subspace approximation classifier (RSAC), is proposed to achieve lightweight continuous learning. RSAC contains a feature reduction module and classifier module with regularization. Extensive experiments show that RSAC is more efficient than prior continuous learning works and outperforms these works on various experimental settings.

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.

Class-Incremental Learning with Topological Schemas of Memory Spaces

Xinyuan Chang, Xiaoyu Tao, Xiaopeng Hong, Xing Wei, Wei Ke, Yihong Gong

Auto-TLDR; Class-incremental Learning with Topological Schematic Model

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Class-incremental learning (CIL) aims to incrementally learn a unified classifier for new classes emerging, which suffers from the catastrophic forgetting problem. To alleviate forgetting and improve the recognition performance, we propose a novel CIL framework, named the topological schemas model (TSM). TSM consists of a Gaussian mixture model arranged on 2D grids (2D-GMM) as the memory of the learned knowledge. To train the 2D-GMM model, we develop a novel competitive expectation-maximization (CEM) method, which contains a global topology embedding step and a local expectation-maximization finetuning step. Meanwhile, we choose the image samples of old classes that have the maximum posterior probability with respect to each Gaussian distribution as the episodic points. When finetuning for new classes, we propose the memory preservation loss (MPL) term to ensure episodic points still have maximum probabilities with respect to the corresponding Gaussian distribution. MPL preserves the distribution of 2D-GMM for old knowledge during incremental learning and alleviates catastrophic forgetting. Comprehensive experimental evaluations on two popular CIL benchmarks CIFAR100 and subImageNet demonstrate the superiority of our TSM.

Dual-Memory Model for Incremental Learning: The Handwriting Recognition Use Case

Mélanie Piot, Bérangère Bourdoulous, Aurelia Deshayes, Lionel Prevost

Auto-TLDR; A dual memory model for handwriting recognition

Abstract Poster Similar

In this paper, we propose a dual memory model inspired by neural science. Short-term memory processes the data stream before integrating them into long-term memory, which generalizes. The use case is learning the ability to recognize handwriting. This begins with the learning of prototypical letters . It continues throughout life and gives the individual the ability to recognize increasingly varied handwriting. This second task is achieved by incrementally training our dual-memory model. We used a convolution network for encoding and random forests as the memory model. Indeed, the latter have the advantage of being easily enhanced to integrate new data and new classes. Performances on the MNIST database are very encouraging since they exceed 95\% and the complexity of the model remains reasonable.

GAN-Based Gaussian Mixture Model Responsibility Learning

Wanming Huang, Yi Da Xu, Shuai Jiang, Xuan Liang, Ian Oppermann

Auto-TLDR; Posterior Consistency Module for Gaussian Mixture Model

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Mixture Model (MM) is a probabilistic framework allows us to define dataset containing $K$ different modes. When each of the modes is associated with a Gaussian distribution, we refer to it as Gaussian MM or GMM. Given a data point $x$, a GMM may assume the existence of a random index $k \in \{1, \dots , K \}$ identifying which Gaussian the particular data is associated with. In a traditional GMM paradigm, it is straightforward to compute in closed-form, the conditional likelihood $p(x |k, \theta)$ as well as the responsibility probability $p(k|x, \theta)$ describing the distribution weights for each data. Computing the responsibility allows us to retrieve many important statistics of the overall dataset, including the weights of each of the modes/clusters. Modern large data-sets are often containing multiple unlabelled modes, such as paintings dataset may contain several styles; fashion images containing several unlabelled categories. In its raw representation, the Euclidean distances between the data (e.g., images) do not allow them to form mixtures naturally, nor it's feasible to compute responsibility distribution analytically, making GMM unable to apply. In this paper, we utilize the Generative Adversarial Network (GAN) framework to achieve a plausible alternative method to compute these probabilities. The key insight is that we compute them at the data's latent space $z$ instead of $x$. However, this process of $z \rightarrow x$ is irreversible under GAN which renders the computation of responsibility $p(k|x, \theta)$ infeasible. Our paper proposed a novel method to solve it by using a so-called Posterior Consistency Module (PCM). PCM acts like a GAN, except its Generator $C_{\text{PCM}}$ does not output the data, but instead it outputs a distribution to approximate $p(k|x, \theta)$. The entire network is trained in an ``end-to-end'' fashion. Trough these techniques, it allows us to model the dataset of very complex structure using GMM and subsequently to discover interesting properties of an unsupervised dataset, including its segments, as well as generating new ``out-distribution" data by smooth linear interpolation across any combinations of the modes in a completely unsupervised manner.

IDA-GAN: A Novel Imbalanced Data Augmentation GAN

Hao Yang, Yun Zhou

Auto-TLDR; IDA-GAN: Generative Adversarial Networks for Imbalanced Data Augmentation

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Class imbalance is a widely existed and challenging problem in real-world applications such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to the scarce of data, it could significantly deteriorate the accuracy of classification. To address this challenge, we propose a novel Imbalanced Data Augmentation Generative Adversarial Networks (GAN) named IDA-GAN as an augmentation tool to deal with the imbalanced dataset. This is a great challenge because it is hard to train a GAN model under this situation. We overcome this issue by coupling Variational autoencoder along with GAN training. Specifically, we introduce the Variational autoencoder to learn the majority and minority class distributions in the latent space, and use the generative model to utilize each class distribution for the subsequent GAN training. The generative model learns useful features to generate target minority-class samples. By comparing with the state-of-the-art GAN models, the experimental results demonstrate that our proposed IDA-GAN could generate more diverse minority samples with better qualities, and it consistently benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets: MNIST, Fashion-MNIST, SVHN, CIFAR-10 and GTRSB.

Boundary Optimised Samples Training for Detecting Out-Of-Distribution Images

Luca Marson, Vladimir Li, Atsuto Maki

Auto-TLDR; Boundary Optimised Samples for Out-of-Distribution Input Detection in Deep Convolutional Networks

Abstract Slides Poster Similar

This paper presents a new approach to the problem of detecting out-of-distribution (OOD) inputs in image classifications with deep convolutional networks. We leverage so-called boundary samples to enforce low confidence (maximum softmax probabilities) for inputs far away from the training data. In particular, we propose the boundary optimised samples (named BoS) training algorithm for generating them. Unlike existing approaches, it does not require extra generative adversarial network, but achieves the goal by simply back propagating the gradient of an appropriately designed loss function to the input samples. At the end of the BoS training, all the boundary samples are in principle located on a specific level hypersurface with respect to the designed loss. Our contributions are i) the BoS training as an efficient alternative to generate boundary samples, ii) a robust algorithm therewith to enforce low confidence for OOD samples, and iii) experiments demonstrating improved OOD detection over the baseline. We show the performance using standard datasets for training and different test sets including Fashion MNIST, EMNIST, SVHN, and CIFAR-100, preceded by evaluations with a synthetic 2-dimensional dataset that provide an insight for the new procedure.

ARCADe: A Rapid Continual Anomaly Detector

Ahmed Frikha, Denis Krompass, Volker Tresp

Auto-TLDR; ARCADe: A Meta-Learning Approach for Continuous Anomaly Detection

Abstract Slides Poster Similar

Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored. The present work addresses a learning scenario where a model has to incrementally learn a sequence of anomaly detection tasks, i.e. tasks from which only examples from the normal (majority) class are available for training. We define this novel learning problem of continual anomaly detection (CAD) and formulate it as a meta-learning problem. Moreover, we propose \emph{A Rapid Continual Anomaly Detector (ARCADe)}, an approach to train neural networks to be robust against the major challenges of this new learning problem, namely catastrophic forgetting and overfitting to the majority class. The results of our experiments on three datasets show that, in the CAD problem setting, ARCADe substantially outperforms baselines from the continual learning and anomaly detection literature. Finally, we provide deeper insights into the learning strategy yielded by the proposed meta-learning algorithm.

A Joint Representation Learning and Feature Modeling Approach for One-Class Recognition

Pramuditha Perera, Vishal Patel

Auto-TLDR; Combining Generative Features and One-Class Classification for Effective One-class Recognition

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One-class recognition is traditionally approached either as a representation learning problem or a feature modelling problem. In this work, we argue that both of these approaches have their own limitations; and a more effective solution can be obtained by combining the two. The proposed approach is based on the combination of a generative framework and a one-class classification method. First, we learn generative features using the one-class data with a generative framework. We augment the learned features with the corresponding reconstruction errors to obtain augmented features. Then, we qualitatively identify a suitable feature distribution that reduces the redundancy in the chosen classifier space. Finally, we force the augmented features to take the form of this distribution using an adversarial framework. We test the effectiveness of the proposed method on three one-class classification tasks and obtain state-of-the-art results.

Energy Minimum Regularization in Continual Learning

Xiaobin Li, Weiqiang Wang

Auto-TLDR; Energy Minimization Regularization for Continuous Learning

Abstract Slides Similar

How to give agents the ability of continuous learning like human and animals is still a challenge. In the regularized continual learning method OWM, the constraint of the model on the energy compression of the learned task is ignored, which results in the poor performance of the method on the dataset with a large number of learning tasks. In this paper, we propose an energy minimization regularization(EMR) method to constrain the energy of learned tasks, providing enough learning space for the following tasks that are not learned, and increasing the capacity of the model to the number of learning tasks. A large number of experiments show that our method can effectively increase the capacity of the model and reduce the sensitivity of the model to the number of tasks and the size of the network.

Selecting Useful Knowledge from Previous Tasks for Future Learning in a Single Network

Feifei Shi, Peng Wang, Zhongchao Shi, Yong Rui

Auto-TLDR; Continual Learning with Gradient-based Threshold Threshold

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Continual learning is able to learn new tasks incrementally while avoiding catastrophic forgetting. Recent work has shown that packing multiple tasks into a single network incrementally by iterative pruning and re-training network is a promising method. We build upon this idea and propose an improved version of PackNet, specifically, we propose a novel gradient-based threshold method to reuse the knowledge of the previous tasks selectively when learning new tasks. Our experiments on a variety of classification tasks and different network architectures demonstrate that our method obtains competitive results when compared to PackNet.

Continuous Learning of Face Attribute Synthesis

Ning Xin, Shaohui Xu, Fangzhe Nan, Xiaoli Dong, Weijun Li, Yuanzhou Yao

Auto-TLDR; Continuous Learning for Face Attribute Synthesis

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The generative adversarial network (GAN) exhibits great superiority in the face attribute synthesis task. However, existing methods have very limited effects on the expansion of new attributes. To overcome the limitations of a single network in new attribute synthesis, a continuous learning method for face attribute synthesis is proposed in this work. First, the feature vector of the input image is extracted and attribute direction regression is performed in the feature space to obtain the axes of different attributes. The feature vector is then linearly guided along the axis so that images with target attributes can be synthesized by the decoder. Finally, to make the network capable of continuous learning, the orthogonal direction modification module is used to extend the newly-added attributes. Experimental results show that the proposed method can endow a single network with the ability to learn attributes continuously, and, as compared to those produced by the current state-of-the-art methods, the synthetic attributes have higher accuracy.

Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks

Denis Huseljic, Bernhard Sick, Marek Herde, Daniel Kottke

Auto-TLDR; AE-DNN: Modeling Uncertainty in Deep Neural Networks

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Despite the success of deep neural networks (DNN) in many applications, their ability to model uncertainty is still significantly limited. For example, in safety-critical applications such as autonomous driving, it is crucial to obtain a prediction that reflects different types of uncertainty to address life-threatening situations appropriately. In such cases, it is essential to be aware of the risk (i.e., aleatoric uncertainty) and the reliability (i.e., epistemic uncertainty) that comes with a prediction. We present AE-DNN, a model allowing the separation of aleatoric and epistemic uncertainty while maintaining a proper generalization capability. AE-DNN is based on deterministic DNN, which can determine the respective uncertainty measures in a single forward pass. In analyses with synthetic and image data, we show that our method improves the modeling of epistemic uncertainty while providing an intuitively understandable separation of risk and reliability.

SAGE: Sequential Attribute Generator for Analyzing Glioblastomas Using Limited Dataset

Padmaja Jonnalagedda, Brent Weinberg, Jason Allen, Taejin Min, Shiv Bhanu, Bir Bhanu

Auto-TLDR; SAGE: Generative Adversarial Networks for Imaging Biomarker Detection and Prediction

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While deep learning approaches have shown remarkable performance in many imaging tasks, most of these methods rely on availability of large quantities of data. Medical image data, however, is scarce and fragmented. Generative Adversarial Networks (GANs) have recently been very effective in handling such datasets by generating more data. If the datasets are very small, however, GANs cannot learn the data distribution properly, resulting in less diverse or low-quality results. One such limited dataset is that for the concurrent gain of 19/20 chromosomes (19/20 co-gain), a mutation with positive prognostic value in Glioblastomas (GBM). In this paper, we detect imaging biomarkers for the mutation to streamline the extensive and invasive prognosis pipeline. Since this mutation is relatively rare, i.e. small dataset, we propose a novel generative framework – the Sequential Attribute GEnerator (SAGE), that generates detailed tumor imaging features while learning from a limited dataset. Experiments show that not only does SAGE generate high quality tumors when compared to standard Deep Convolutional GAN (DC-GAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP), it also captures the imaging biomarkers accurately.

Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation

Hai Tran, Sumyeong Ahn, Taeyoung Lee, Yung Yi

Auto-TLDR; Unsupervised Domain Adaptation using Artificial Classes

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We study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and unlabeled data from the target domain. There exists an array of recent research based on the idea of extracting features that are not only invariant for both domains but also provide high discriminative power for the target domain. In this paper, we propose an idea of improving the discriminativeness: Adding an extra artificial class and training the model on the given data together with the GAN-generated samples of the new class. The trained model based on the new class samples is capable of extracting the features that are more discriminative by repositioning data of current classes in the target domain and therefore increasing the distances among the target clusters in the feature space. Our idea is highly generic so that it is compatible with many existing methods such as DANN, VADA, and DIRT-T. We conduct various experiments for the standard data commonly used for the evaluation of unsupervised domain adaptations and demonstrate that our algorithm achieves the SOTA performance for many scenarios.

Naturally Constrained Online Expectation Maximization

Daniela Pamplona, Antoine Manzanera

Auto-TLDR; Constrained Online Expectation-Maximization for Probabilistic Principal Components Analysis

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With the rise of big data sets, learning algorithms must be adapted to piece-wise mechanisms in order to tackle time and memory costs of large scale calculations. Furthermore, for most learning embedded systems the input data are fed in a sequential and contingent manner: one by one, and possibly class by class. Thus, learning algorithms should not only run online but cope with time-varying, non-independent, and non-balanced training data for the system's entire life. Online Expectation-Maximization is a well-known algorithm for learning probabilistic models in real-time, due to its simplicity and convergence properties. However, these properties are only valid in the case of large, independent and identically distributed (iid) samples. In this paper, we propose to constraint the online Expectation-Maximization on the Fisher distance between the parameters. After the presentation of the algorithm, we make a thorough study of its use in Probabilistic Principal Components Analysis. First, we derive the update rules, then we analyse the effect of the constraint on major problems of online and sequential learning: convergence, forgetting and interference. Furthermore we use several algorithmic protocols: iid {\em vs} sequential data, and constraint parameters updated step-wise {\em vs} class-wise. Our results show that this constraint increases the convergence rate of online Expectation-Maximization, decreases forgetting and slightly introduces transfer learning.

On the Evaluation of Generative Adversarial Networks by Discriminative Models

Amirsina Torfi, Mohammadreza Beyki, Edward Alan Fox

Auto-TLDR; Domain-agnostic GAN Evaluation with Siamese Neural Networks

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Generative Adversarial Networks (GANs) can accurately model complex multi-dimensional data and generate realistic samples. However, due to their implicit estimation of data distributions, their evaluation is a challenging task. The majority of research efforts associated with tackling this issue were validated by qualitative visual evaluation. Such approaches do not generalize well beyond the image domain. Since many of those evaluation metrics are proposed and bound to the vision domain, they are difficult to apply to other domains. Quantitative measures are necessary to better guide the training and comparison of different GANs models. In this work, we leverage Siamese neural networks to propose a domain-agnostic evaluation metric: (1) with a qualitative evaluation that is consistent with human evaluation, (2) that is robust relative to common GAN issues such as mode dropping and invention, and (3) does not require any pretrained classifier. The empirical results in this paper demonstrate the superiority of this method compared to the popular Inception Score and are competitive with the FID score.

Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis

Avinash Madasu, Anvesh Rao Vijjini

Auto-TLDR; Sequential Domain Adaptation using Elastic Weight Consolidation for Sentiment Analysis

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Elastic Weight Consolidation (EWC) is a technique used in overcoming catastrophic forgetting between successive tasks trained on a neural network. We use this phenomenon of information sharing between tasks for domain adaptation. Training data for tasks such as sentiment analysis (SA) may not be fairly represented across multiple domains. Domain Adaptation (DA) aims to build algorithms that leverage information from source domains to facilitate performance on an unseen target domain. We propose a model-independent framework - Sequential Domain Adaptation (SDA). SDA draws on EWC for training on successive source domains to move towards a general domain solution, thereby solving the problem of domain adaptation. We test SDA on convolutional, recurrent and attention-based architectures. Our experiments show that the proposed framework enables simple architectures such as CNNs to outperform complex state-of-the-art models in domain adaptation of SA. We further observe the effectiveness of a harder first Anti-Curriculum ordering of source domains leads to maximum performance.

Phase Retrieval Using Conditional Generative Adversarial Networks

Tobias Uelwer, Alexander Oberstraß, Stefan Harmeling

Auto-TLDR; Conditional Generative Adversarial Networks for Phase Retrieval

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In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems. We show that including knowledge of the measurement process at training time leads to an optimization at test time that is more robust to initialization than existing approaches involving generative models. In addition, conditioning the generator network on the measurements enables us to achieve much more detailed results. We empirically demonstrate that these advantages provide meaningful solutions to the Fourier and the compressive phase retrieval problem and that our method outperforms well-established projection-based methods as well as existing methods that are based on neural networks. Like other deep learning methods, our approach is very robust to noise and can therefore be very useful for real-world applications.

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

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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.

Leveraging Synthetic Subject Invariant EEG Signals for Zero Calibration BCI

Nik Khadijah Nik Aznan, Amir Atapour-Abarghouei, Stephen Bonner, Jason Connolly, Toby Breckon

Auto-TLDR; SIS-GAN: Subject Invariant SSVEP Generative Adversarial Network for Brain-Computer Interface

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Recently, substantial progress has been made in the area of Brain-Computer Interface (BCI) using modern machine learning techniques to decode and interpret brain signals. While Electroencephalography (EEG) has provided a non-invasive method of interfacing with a human brain, the acquired data is often heavily subject and session dependent. This makes seamless incorporation of such data into real-world applications intractable as the subject and session data variance can lead to long and tedious calibration requirements and cross-subject generalisation issues. Focusing on a Steady State Visual Evoked Potential (SSVEP) classification systems, we propose a novel means of generating highly-realistic synthetic EEG data invariant to any subject, session or other environmental conditions. Our approach, entitled the Subject Invariant SSVEP Generative Adversarial Network (SIS-GAN), produces synthetic EEG data from multiple SSVEP classes using a single network. Additionally, by taking advantage of a fixed-weight pre-trained subject classification network, we ensure that our generative model remains agnostic to subject-specific features and thus produces subject-invariant data that can be applied to new previously unseen subjects. Our extensive experimental evaluation demonstrates the efficacy of our synthetic data, leading to superior performance, with improvements of up to 16% in zero-calibration classification tasks when trained using our subject-invariant synthetic EEG signals.

Learning with Delayed Feedback

Pranavan Theivendiram, Terence Sim

Auto-TLDR; Unsupervised Machine Learning with Delayed Feedback

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We propose a novel supervised machine learning strategy, inspired by human learning, that enables an Agent to learn continually over its lifetime. A natural consequence is that the Agent must be able to handle an input whose label is delayed until a later time, or may not arrive at all. Our Agent learns in two steps: a short Seeding phase, in which the Agent's model is initialized with labelled inputs, and an indefinitely long Growing phase, in which the Agent refines and assesses its model if the label is given for an input, but stores the input in a finite-length queue if the label is missing. Queued items are matched against future input-label pairs that arrive, and the model is then updated. Our strategy also allows for the delayed feedback to take a different form. For example, in an image captioning task, the feedback could be a semantic segmentation rather than a textual caption. We show with many experiments that our strategy enables an Agent to learn flexibly and efficiently.

Hierarchical Mixtures of Generators for Adversarial Learning

Alper Ahmetoğlu, Ethem Alpaydin

Auto-TLDR; Hierarchical Mixture of Generative Adversarial Networks

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Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distri- bution. There is a generator that takes a latent vector as input and transforms it into a valid sample from the distribution. There is also a discriminator that is trained to discriminate such fake samples from true samples of the distribution; at the same time, the generator is trained to generate fakes that the discriminator cannot tell apart from the true samples. Instead of learning a global generator, a recent approach involves training multiple generators each responsible from one part of the distribution. In this work, we review such approaches and propose the hierarchical mixture of generators, inspired from the hierarchical mixture of experts model, that learns a tree structure implementing a hierarchical clustering with soft splits in the decision nodes and local generators in the leaves. Since the generators are combined softly, the whole model is continuous and can be trained using gradient-based optimization, just like the original GAN model. Our experiments on five image data sets, namely, MNIST, FashionMNIST, UTZap50K, Oxford Flowers, and CelebA, show that our proposed model generates samples of high quality and diversity in terms of popular GAN evaluation metrics. The learned hierarchical structure also leads to knowledge extraction.

Variational Inference with Latent Space Quantization for Adversarial Resilience

Vinay Kyatham, Deepak Mishra, Prathosh A.P.

Auto-TLDR; A Generalized Defense Mechanism for Adversarial Attacks on Data Manifolds

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Despite their tremendous success in modelling highdimensional data manifolds, deep neural networks suffer from the threat of adversarial attacks - Existence of perceptually valid input-like samples obtained through careful perturbation that lead to degradation in the performance of the underlying model. Major concerns with existing defense mechanisms include non-generalizability across different attacks, models and large inference time. In this paper, we propose a generalized defense mechanism capitalizing on the expressive power of regularized latent space based generative models. We design an adversarial filter, devoid of access to classifier and adversaries, which makes it usable in tandem with any classifier. The basic idea is to learn a Lipschitz constrained mapping from the data manifold, incorporating adversarial perturbations, to a quantized latent space and re-map it to the true data manifold. Specifically, we simultaneously auto-encode the data manifold and its perturbations implicitly through the perturbations of the regularized and quantized generative latent space, realized using variational inference. We demonstrate the efficacy of the proposed formulation in providing resilience against multiple attack types (black and white box) and methods, while being almost real-time. Our experiments show that the proposed method surpasses the stateof-the-art techniques in several cases.

Verifying the Causes of Adversarial Examples

Honglin Li, Yifei Fan, Frieder Ganz, Tony Yezzi, Payam Barnaghi

Auto-TLDR; Exploring the Causes of Adversarial Examples in Neural Networks

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The robustness of neural networks is challenged by adversarial examples that contain almost imperceptible perturbations to inputs which mislead a classifier to incorrect outputs in high confidence. Limited by the extreme difficulty in examining a high-dimensional image space thoroughly, research on explaining and justifying the causes of adversarial examples falls behind studies on attacks and defenses. In this paper, we present a collection of potential causes of adversarial examples and verify (or partially verify) them through carefully-designed controlled experiments. The major causes of adversarial examples include model linearity, one-sum constraint, and geometry of the categories. To control the effect of those causes, multiple techniques are applied such as $L_2$ normalization, replacement of loss functions, construction of reference datasets, and novel models using multi-layer perceptron probabilistic neural networks (MLP-PNN) and density estimation (DE). Our experiment results show that geometric factors tend to be more direct causes and statistical factors magnify the phenomenon, especially for assigning high prediction confidence. We hope this paper will inspire more studies to rigorously investigate the root causes of adversarial examples, which in turn provide useful guidance on designing more robust models.

Generative Latent Implicit Conditional Optimization When Learning from Small Sample

Idan Azuri, Daphna Weinshall

Auto-TLDR; GLICO: Generative Latent Implicit Conditional Optimization for Small Sample Learning

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We revisit the long-standing problem of learning from small sample. The generation of new samples from a small training set of labeled points has attracted increased attention in recent years. In this paper, we propose a novel such method called GLICO (Generative Latent Implicit Conditional Optimization). GLICO learns a mapping from the training examples to a latent space and a generator that generates images from vectors in the latent space. Unlike most recent work, which rely on access to large amounts of unlabeled data, GLICO does not require access to any additional data other than the small set of labeled points. In fact, GLICO learns to synthesize completely new samples for every class using as little as 5 or 10 examples per class, with as few as 10 such classes and no data from unknown classes. GLICO is then used to augment the small training set while training a classifier on the small sample. To this end, our proposed method samples the learned latent space using spherical interpolation (slerp) and generates new examples using the trained generator. Empirical results show that the new sampled set is diverse enough, leading to improvement in image classification in comparison with the state of the art when trained on small samples obtained from CIFAR-10, CIFAR-100, and CUB-200.

GAP: Quantifying the Generative Adversarial Set and Class Feature Applicability of Deep Neural Networks

Edward Collier, Supratik Mukhopadhyay

Auto-TLDR; Approximating Adversarial Learning in Deep Neural Networks Using Set and Class Adversaries

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Recent work in deep neural networks has sought to characterize the nature in which a network learns features and how applicable learnt features are to various problem sets. Deep neural network applicability can be split into three sub-problems; set applicability, class applicability, and instance applicability. In this work we seek to quantify the applicability of features learned during adversarial training, focusing specifically on set and class applicability. We apply techniques for measuring applicability to both generators and discriminators trained on various data sets to quantify applicability and better observe how both a generator and a discriminator, and generative models as a whole, learn features during adversarial training.

Overcoming Noisy and Irrelevant Data in Federated Learning

Tiffany Tuor, Shiqiang Wang, Bong Jun Ko, Changchang Liu, Kin K Leung

Auto-TLDR; Distributedly Selecting Relevant Data for Federated Learning

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Many image and vision applications require a large amount of data for model training. Collecting all such data at a central location can be challenging due to data privacy and communication bandwidth restrictions. Federated learning is an effective way of training a machine learning model in a distributed manner from local data collected by client devices, which does not require exchanging the raw data among clients. A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training. Therefore, before starting the learning process, it is important to select the subset of data that is relevant to the given federated learning task. In this paper, we propose a method for distributedly selecting relevant data, where we use a benchmark model trained on a small benchmark dataset that is task-specific, to evaluate the relevance of individual data samples at each client and select the data with sufficiently high relevance. Then, each client only uses the selected subset of its data in the federated learning process. The effectiveness of our proposed approach is evaluated on multiple real-world image datasets in a simulated system with a large number of clients, showing up to 25% improvement in model accuracy compared to training with all data.

Signal Generation Using 1d Deep Convolutional Generative Adversarial Networks for Fault Diagnosis of Electrical Machines

Russell Sabir, Daniele Rosato, Sven Hartmann, Clemens Gühmann

Auto-TLDR; Large Dataset Generation from Faulty AC Machines using Deep Convolutional GAN

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AC machines may be subjected to different electrical or mechanical faults during their operation. Fault patterns can be detected in the DC current from the machine’s E-Drive system with the help of Deep or Machine Learning algorithms. However, Deep or Machine Learning algorithms require large amounts of dataset for training and without the availability of a large dataset the algorithms fail to generalize or give their optimal performance. Collecting large amounts of data from faulty machine can be a tedious task. It is expensive and not always possible. In some cases, the machine is completely damaged even before sufficient amount of data can be collected. Also, data collection from defected machine may cause permanent damage to the connected system. Therefore, in this paper the problem of small dataset is tackled by presenting a methodology for large dataset generation by using the well-known generative model, Generative Adversarial Networks (GAN). As an example, the stator open circuit fault in a synchronous machine is considered. DC currents from the machine’s E-Drive system are measured from different healthy and faulty machines and are used for training of two 1d DCGANs (Deep Convolutional GANs), one for the healthy and the other for the current signal from the faulty machine. Conventional GANs are difficult to train, however in this paper, training parameters of 1d DCGAN are tuned which results an improved training process. The performance of generator during the training of 1d DCGAN is evaluated by using the Fréchet Inception Distance (FID) metric. The proposed 1d DCGAN model is said to converge when FID score between the real and generated signal reaches below a certain threshold. The generated signals from the trained 1d DCGAN are further evaluated using the PDF (Probability Density Function), frequency domain analysis and other measures which check for duplication of the real data and their statistical diversity. The trained 1d DCGAN is able to generate DC current signals for building large datasets for the training of Deep or Machine learning models.

Beyond Cross-Entropy: Learning Highly Separable Feature Distributions for Robust and Accurate Classification

Arslan Ali, Andrea Migliorati, Tiziano Bianchi, Enrico Magli

Auto-TLDR; Gaussian class-conditional simplex loss for adversarial robust multiclass classifiers

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Deep learning has shown outstanding performance in several applications including image classification. However, deep classifiers are known to be highly vulnerable to adversarial attacks, in that a minor perturbation of the input can easily lead to an error. Providing robustness to adversarial attacks is a very challenging task especially in problems involving a large number of classes, as it typically comes at the expense of an accuracy decrease. In this work, we propose the Gaussian class-conditional simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that provides adversarial robustness while at the same time achieving or even surpassing the classification accuracy of state-of-the-art methods. Differently from other frameworks, the proposed method learns a mapping of the input classes onto target distributions in a latent space such that the classes are linearly separable. Instead of maximizing the likelihood of target labels for individual samples, our objective function pushes the network to produce feature distributions yielding high inter-class separation. The mean values of the distributions are centered on the vertices of a simplex such that each class is at the same distance from every other class. We show that the regularization of the latent space based on our approach yields excellent classification accuracy and inherently provides robustness to multiple adversarial attacks, both targeted and untargeted, outperforming state-of-the-art approaches over challenging datasets.

Investigation of DNN Model Robustness Using Heterogeneous Datasets

Wen-Hung Liao, Yen-Ting Huang

Auto-TLDR; Evaluating the Dependency of Deep Learning on Heterogeneous Data Set for Learning

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Deep learning framework has been successfully applied to tackle many challenging tasks in pattern recognition and computer vision thanks to its ability to automatically extract representative features from the training data. Such type of data-driven approach, however, is subject to the criticism of too much dependency on the training set. In this research, we attempt to investigate the validity of this statement: ‘deep learning is only as good as its data’ by evaluating the performance of deep learning models using heterogeneous data sets, in which distinct representations of the same source data are employed for training/testing. We have examined three cases: low-resolution image, severely compressed input and halftone image in this work. Our preliminary results indicate that such dependency indeed exists. Classifier performance drops considerably when the model is tested with modified or transformed input. The best outcomes are obtained when the model is trained with hybrid input.

NeuralFP: Out-Of-Distribution Detection Using Fingerprints of Neural Networks

Wei-Han Lee, Steve Millman, Nirmit Desai, Mudhakar Srivatsa, Changchang Liu

Auto-TLDR; NeuralFP: Detecting Out-of-Distribution Records Using Neural Network Models

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Edge devices use neural network models learnt on cloud to predict labels of its data records, which may lead to incorrect predictions especially for records that are different from the data involved in the training process, i.e., out-of-distribution (OOD) records. However, recent efforts in OOD detection either require the retraining of the model or assume the existence of a certain amount of OOD records, thus limiting their application in practice. In this work, we propose a novel OOD detection method (named as NeuralFP) without requiring any access to OOD records, which constructs non-linear fingerprints of neural network models memorizing the information of data observed during training. The key idea of NeuralFP is to exploit the difference in how the neural network model responds to data records in its training set versus data records that are anomalous. Specifically, NeuralFP builds autoencoders for each layer of the neural network model and then carefully analyzes the error distribution of the autocoders in reconstructing the training set to identify OOD records. Through extensive experiments on multiple real-world datasets, we show the effectiveness of NeuralFP in detecting OOD records as well as its advantages over previous approaches. Furthermore, we provide useful guidelines for parameter selection in the practical adoption of NeuralFP.

Interpreting the Latent Space of GANs Via Correlation Analysis for Controllable Concept Manipulation

Ziqiang Li, Rentuo Tao, Hongjing Niu, Bin Li

Auto-TLDR; Exploring latent space of GANs by analyzing correlation between latent variables and semantic contents in generated images

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Generative adversarial nets (GANs) have been successfully applied in many fields like image generation, inpainting, super-resolution and drug discovery, etc., by now, the inner process of GANs is far from been understood. To get deeper insight of the intrinsic mechanism of GANs, in this paper, a method for interpreting the latent space of GANs by analyzing the correlation between latent variables and the corresponding semantic contents in generated images is proposed. Unlike previous methods that focus on dissecting models via feature visualization, the emphasis of this work is put on the variables in latent space, i.e. how the latent variables affect the quantitative analysis of generated results. Given a pretrained GAN model with weights fixed, the latent variables are intervened to analyze their effect on the semantic content in generated images. A set of controlling latent variables can be derived for specific content generation, and the controllable semantic content manipulation be achieved. The proposed method is testified on the datasets Fashion-MNIST and UT Zappos50K, experiment results show its effectiveness

Iterative Label Improvement: Robust Training by Confidence Based Filtering and Dataset Partitioning

Christian Haase-Schütz, Rainer Stal, Heinz Hertlein, Bernhard Sick

Auto-TLDR; Meta Training and Labelling for Unlabelled Data

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State-of-the-art, high capacity deep neural networks not only require large amounts of labelled training data, they are also highly susceptible to labelling errors in this data, typically resulting in large efforts and costs and therefore limiting the applicability of deep learning. To alleviate this issue, we propose a novel meta training and labelling scheme that is able to use inexpensive unlabelled data by taking advantage of the generalization power of deep neural networks. We show experimentally that by solely relying on one network architecture and our proposed scheme of combining self-training with pseudolabels, both label quality and resulting model accuracy, can be improved significantly. Our method achieves state-of-the-art results, while being architecture agnostic and therefore broadly applicable. Compared to other methods dealing with erroneous labels, our approach does neither require another network to be trained, nor does it necessarily need an additional, highly accurate reference label set. Instead of removing samples from a labelled set, our technique uses additional sensor data without the need for manual labelling. Furthermore, our approach can be used for semi-supervised learning.

On-Manifold Adversarial Data Augmentation Improves Uncertainty Calibration

Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang

Auto-TLDR; On-Manifold Adversarial Data Augmentation for Uncertainty Estimation

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Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks. To improve uncertainty estimation, we propose On-Manifold Adversarial Data Augmentation or OMADA, which specifically attempts to generate challenging examples by following an on-manifold adversarial attack path in the latent space of an autoencoder that closely approximates the decision boundaries between classes. On a variety of datasets and for multiple network architectures, OMADA consistently yields more accurate and better calibrated classifiers than baseline models, and outperforms competing approaches such as Mixup, as well as achieving similar performance to (at times better than) post-processing calibration methods such as temperature scaling. Variants of OMADA can employ different sampling schemes for ambiguous on-manifold examples based on the entropy of their estimated soft labels, which exhibit specific strengths for generalization, calibration of predicted uncertainty, or detection of out-of-distribution inputs.

CardioGAN: An Attention-Based Generative Adversarial Network for Generation of Electrocardiograms

Subhrajyoti Dasgupta, Sudip Das, Ujjwal Bhattacharya

Auto-TLDR; CardioGAN: Generative Adversarial Network for Synthetic Electrocardiogram Signals

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Electrocardiogram (ECG) signal is studied to obtain crucial information about the condition of a patient's heart. Machine learning based automated medical diagnostic systems that may help to evaluate the condition of the heart from this signal are required to be trained using large volumes of labelled training samples and the same may increase the chance of compromising with the patients' privacy. To solve this issue, generation of synthetic electrocardiogram signals by learning only from the general distributions of the available real training samples have been attempted in the literature. However, these studies did not pay necessary attention to the specific vital details of these signals, such as the P wave, the QRS complex, and the T wave. This shortcoming often results in the generation of unrealistic synthetic signals, such as a signal which does not contain one or more of the above components. In the present study, a novel deep generative architecture, termed as CardioGAN, based on generative adversarial network and powered by the effective attention mechanism has been designed which is capable of learning the intricate inter-dependencies among the various parts of real samples leading to the generation of more realistic electrocardiogram signals. Also, it helps in reducing the risk of breaching the privacy of patients. Extensive experimentation performed by us establishes that the proposed method achieves a better performance in generating synthetic electrocardiogram signals in comparison to the existing methods. The source code will be made available on github.

A Close Look at Deep Learning with Small Data

Lorenzo Brigato, Luca Iocchi

Auto-TLDR; Low-Complex Neural Networks for Small Data Conditions

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In this work, we perform a wide variety of experiments with different Deep Learning architectures in small data conditions. We show that model complexity is a critical factor when only a few samples per class are available. Differently from the literature, we improve the state of the art using low complexity models. We show that standard convolutional neural networks with relatively few parameters are effective in this scenario. In many of our experiments, low complexity models outperform state-of-the-art architectures. Moreover, we propose a novel network that uses an unsupervised loss to regularize its training. Such architecture either improves the results either performs comparably well to low capacity networks. Surprisingly, experiments show that the dynamic data augmentation pipeline is not beneficial in this particular domain. Statically augmenting the dataset might be a promising research direction while dropout maintains its role as a good regularizer.

AVAE: Adversarial Variational Auto Encoder

Antoine Plumerault, Hervé Le Borgne, Celine Hudelot

Auto-TLDR; Combining VAE and GAN for Realistic Image Generation

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Among the wide variety of image generative models, two models stand out: Variational Auto Encoders (VAE) and Generative Adversarial Networks (GAN). GANs can produce realistic images, but they suffer from mode collapse and do not provide simple ways to get the latent representation of an image. On the other hand, VAEs do not have these problems, but they often generate images less realistic than GANs. In this article, we explain that this lack of realism is partially due to a common underestimation of the natural image manifold dimensionality. To solve this issue we introduce a new framework that combines VAE and GAN in a novel and complementary way to produce an auto-encoding model that keeps VAEs properties while generating images of GAN-quality. We evaluate our approach both qualitatively and quantitatively on five image datasets.

Background Invariance by Adversarial Learning

Ricardo Cruz, Ricardo M. Prates, Eduardo F. Simas Filho, Joaquim F. Pinto Costa, Jaime S. Cardoso

Auto-TLDR; Improving Convolutional Neural Networks for Overhead Power Line Insulators Detection using a Drone

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Convolutional neural networks are shown to be vulnerable to changes in the background. The proposed method is an end-to-end method that augments the training set by introducing new backgrounds during the training process. These backgrounds are created by a generative network that is trained as an adversary to the model. A case study is explored based on overhead power line insulators detection using a drone – a training set is prepared from photographs taken inside a laboratory and then evaluated using photographs that are harder to collect from outside the laboratory. The proposed method improves performance by over 20% for this case study.

Evaluation of Anomaly Detection Algorithms for the Real-World Applications

Marija Ivanovska, Domen Tabernik, Danijel Skocaj, Janez Pers

Auto-TLDR; Evaluating Anomaly Detection Algorithms for Practical Applications

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Anomaly detection in complex data structures is oneof the most challenging problems in computer vision. In manyreal-world problems, for example in the quality control in modernmanufacturing, the anomalous samples are usually rare, resultingin (highly) imbalanced datasets. However, in current researchpractice, these scenarios are rarely modeled, and as a conse-quence, evaluation of anomaly detection algorithms often do notreproduce results that are useful for practical applications. First,even in case of highly unbalanced input data, anomaly detectionalgorithms are expected to significantly reduce the proportionof anomalous samples, detecting ”almost all” anomalous samples(with exact specifications depending on the target customer). Thisplaces high importance on only the small part of the ROC curve,possibly rendering the standard metrics such as AUC (AreaUnder Curve) and AP (Average Precision) useless. Second, thetarget of automatic anomaly detection in practical applicationsis significant reduction in manual work required, and standardmetrics are poor predictor of this feature. Finally, the evaluationmay produce erratic results for different randomly initializedtraining runs of the neural network, producing evaluation resultsthat may not reproduce well in practice. In this paper, we presentan evaluation methodology that avoids these pitfalls.

Pretraining Image Encoders without Reconstruction Via Feature Prediction Loss

Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki

Auto-TLDR; Feature Prediction Loss for Autoencoder-based Pretraining of Image Encoders

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This work investigates three methods for calculating loss for autoencoder-based pretraining of image encoders: The commonly used reconstruction loss, the more recently introduced deep perceptual similarity loss, and a feature prediction loss proposed here; the latter turning out to be the most efficient choice. Standard auto-encoder pretraining for deep learning tasks is done by comparing the input image and the reconstructed image. Recent work shows that predictions based on embeddings generated by image autoencoders can be improved by training with perceptual loss, i.e., by adding a loss network after the decoding step. So far the autoencoders trained with loss networks implemented an explicit comparison of the original and reconstructed images using the loss network. However, given such a loss network we show that there is no need for the time-consuming task of decoding the entire image. Instead, we propose to decode the features of the loss network, hence the name ``feature prediction loss''. To evaluate this method we perform experiments on three standard publicly available datasets (LunarLander-v2, STL-10, and SVHN) and compare six different procedures for training image encoders (pixel-wise, perceptual similarity, and feature prediction losses; combined with two variations of image and feature encoding/decoding). The embedding-based prediction results show that encoders trained with feature prediction loss is as good or better than those trained with the other two losses. Additionally, the encoder is significantly faster to train using feature prediction loss in comparison to the other losses. The method implementation used in this work is available online: https://github.com/guspih/Perceptual-Autoencoders

S2I-Bird: Sound-To-Image Generation of Bird Species Using Generative Adversarial Networks

Joo Yong Shim, Joongheon Kim, Jong-Kook Kim

Auto-TLDR; Generating bird images from sound using conditional generative adversarial networks

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Generating images from sound is a challenging task. This paper proposes a novel deep learning model that generates bird images from their corresponding sound information. Our proposed model includes a sound encoder in order to extract suitable feature representations from audio recordings, and then it generates bird images that corresponds to its calls using conditional generative adversarial networks (GANs) with auxiliary classifiers. We demonstrate that our model produces better image generation results which outperforms other state-of-the-art methods in a similar context.

Knowledge Distillation with a Precise Teacher and Prediction with Abstention

Xu Yi, Jian Pu, Hui Zhao

Auto-TLDR; Knowledge Distillation using Deep gambler loss and selective classification framework

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Knowledge distillation, which aims to train model under the supervision from another large model (teacher model) to the original model (student model), has achieved remarkable results in supervised learning. However, there are two major problems with existing knowledge distillation methods. One is the teacher's supervision is sometimes misleading, and the other is the student's prediction is not accurate enough. To address the first issue, instead of learning a combination of both teachers and ground truth, we apply knowledge adjustment to correct teachers' supervision using ground truth. For the second problem, we use the selective classification framework to train the student model. In particular, the deep gambler loss is adopted to predict with reservation by explicitly introducing the $(m+1)$-th class. We consider two settings of knowledge distillation: (1) distillation across different network structures ({\it AlexNet, ResNet}), and (2) distillation across networks with different depths ({\it ResNet18, ResNet50}) to evaluate the effectiveness of our method. The experimental results on benchmark datasets (i.e., {\it Fashion-MNIST, SVHN, CIFAR10, CIFAR100}) are reported with higher prediction accuracies and lower coverage errors.

Disentangle, Assemble, and Synthesize: Unsupervised Learning to Disentangle Appearance and Location

Hiroaki Aizawa, Hirokatsu Kataoka, Yutaka Satoh, Kunihito Kato

Auto-TLDR; Generative Adversarial Networks with Structural Constraint for controllability of latent space

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The next step for the generative adversarial networks~(GAN) is to learn representations that allow us to control only a certain factor in the image explicitly. Since such a representation of the factor is independent of other factors, the controllability obtained from these representations leads to interpretability by identifying the variation of the synthesized image and the transferability for downstream tasks by inference. However, since it is difficult to identify and strictly define latent factors, the annotation is laborious. Moreover, learning such representations by a GAN is challenging due to the complex generation process. Therefore, we resolve this limitation using a novel generative model that can disentangle latent space into the appearance, the x-axis, and the y-axis of the object, and reassemble these components in an unsupervised manner. Specifically, based on the concept of packing the appearance and location in each position of the feature map, we introduce a novel structural constraint technique that prevents these representations from interacting with each other. The proposed structural constraint promotes the disentanglement of these factors. In experiments, we found that the proposed method is simple but effective for controllability and allows us to control the appearance and location via latent space without supervision, as compared with the conditional GAN.

Uncertainty-Aware Data Augmentation for Food Recognition

Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khatun, Marc Bolaños, Petia Radeva

Auto-TLDR; Data Augmentation for Food Recognition Using Epistemic Uncertainty

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Food recognition has recently attracted attention of many researchers. However, high food ambiguity, inter-class variability and intra-class similarity define a real challenge for the Deep learning and Computer Vision algorithms. In order to improve their performance, it is necessary to better understand what the model learns and, from this, to determine the type of data that should be additionally included for being the most beneficial to the training procedure. In this paper, we propose a new data augmentation strategy that estimates and uses the epistemic uncertainty to guide the model training. The method follows an active learning framework, where the new synthetic images are generated from the hard to classify real ones present in the training data based on the epistemic uncertainty. Hence, it allows the food recognition algorithm to focus on difficult images in order to learn their discriminatives features. On the other hand, avoiding data generation from images that do not contribute to the recognition makes it faster and more efficient. We show that the proposed method allows to improve food recognition and provides a better trade-off between micro- and macro-recall measures.