@article {5011, title = {Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations}, number = {119}, year = {2022}, month = {01/2022}, abstract = {

The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations and illuminations, Deep Neural Networks (DNNs) severely suffer in this case, even when large amounts of training examples are available. In this paper, we investigate three different approaches to improve DNNs in recognizing objects in OoD orientations and illuminations. Namely, these are (i) training much longer after convergence of the in-distribution (InD) validation accuracy, i.e., late-stopping, (ii) tuning the momentum parameter of the batch normalization layers, and (iii) enforcing invariance of the neural activity in an intermediate layer to orientation and illumination conditions. Each of these approaches substantially improves the DNN{\textquoteright}s OoD accuracy (more than 20\% in some cases). We report results in four datasets: two datasets are modified from the MNIST and iLab datasets, and the other two are novel (one of 3D rendered cars and another of objects taken from various controlled orientations and illumination conditions). These datasets allow to study the effects of different amounts of bias and are challenging as DNNs perform poorly in OoD conditions. Finally, we demonstrate that even though the three approaches focus on different aspects of DNNs, they all tend to lead to the same underlying neural mechanism to enable OoD accuracy gains {\textendash} individual neurons in the intermediate layers become more selective to a category and also invariant to OoD orientations and illuminations. We anticipate this study to be a basis for further improvement of deep neural networks{\textquoteright} OoD generalization performance, which is highly demanded to achieve safe and fair AI applications.

}, author = {Akira Sakai and Taro Sunagawa and Spandan Madan and Kanata Suzuki and Takashi Katoh and Hiromichi Kobashi and Hanspeter Pfister and Pawan Sinha and Xavier Boix and Tomotake Sasaki} } @article {5015, title = {Transformer Module Networks for Systematic Generalization in Visual Question Answering}, number = {121}, year = {2022}, month = {02/2022}, abstract = {Transformer-based models achieve great performance on Visual Question Answering (VQA). How- ever, when we evaluate them on systematic generalization, i.e., handling novel combinations of known concepts, their performance degrades. Neural Module Networks (NMNs) are a promising approach for systematic generalization that consists on composing modules, i.e., neural networks that tackle a sub-task. Inspired by Transformers and NMNs, we propose Transformer Module Network (TMN), a novel Transformer-based model for VQA that dynamically composes modules into a question-specific Transformer network. TMNs achieve state-of-the-art systematic generalization performance in three VQA datasets, namely, CLEVR-CoGenT, CLOSURE and GQA-SGL, in some cases improving more than 30\% over standard Transformers. }, author = {Moyuru Yamada and Vanessa D{\textquoteright}Amario and Kentaro Takemoto and Xavier Boix and Tomotake Sasaki} } @conference {5045, title = {Frivolous Units: Wider Networks Are Not Really That Wide}, booktitle = {AAAI 2021}, year = {2021}, month = {05/2021}, abstract = {

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network width is increased. Recent evidence suggests that developing compressible representations allows the complex- ity of large networks to be adjusted for the learning task at hand. However, these representations are poorly understood. A promising strand of research inspired from biology involves studying representations at the unit level as it offers a more granular interpretation of the neural mechanisms. In order to better understand what facilitates increases in width without decreases in accuracy, we ask: Are there mechanisms at the unit level by which networks control their effective complex- ity? If so, how do these depend on the architecture, dataset, and hyperparameters? We identify two distinct types of {\textquotedblleft}frivolous{\textquotedblright} units that prolifer- ate when the network{\textquoteright}s width increases: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be ex- pressed as a linear combination of others. These units imply complexity constraints as the function the network computes could be expressed without them. We also identify how the development of these units can be influenced by architecture and a number of training factors. Together, these results help to explain why the accuracy of DNNs does not degrade when width is increased and highlight the importance of frivolous units toward understanding implicit regularization in DNNs.

}, url = {https://dblp.org/rec/conf/aaai/CasperBDGSVK21.html}, author = {Stephen Casper and Xavier Boix and Vanessa D{\textquoteright}Amario and Ling Guo and Martin Schrimpf and Vinken, Kasper and Gabriel Kreiman} } @article {4583, title = {On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations}, year = {2020}, month = {07/2020}, abstract = {

Recognizing an object{\textquoteright}s category and pose lies at the heart of visual understanding. Recent works suggest that deep neural networks (DNNs) often fail to generalize to category-pose combinations not seen during training. However, it is unclear when and how such generalization may be possible. Does the number of combinations seen during training impact generalization? Is it better to learn category and pose in separate networks, or in a single shared network? Furthermore, what are the neural mechanisms that drive the network{\textquoteright}s generalization? In this paper, we answer these questions by analyzing state-of-the-art DNNs trained to recognize both object category and pose (position, scale, and 3D viewpoint) with quantitative control over the number of category-pose combinations seen during training. We also investigate the emergence of two types of specialized neurons that can explain generalization to unseen combinations{\textemdash}neurons selective to category and invariant to pose, and vice versa. We perform experiments on MNIST extended with position or scale, the iLab dataset with vehicles at different viewpoints, and a challenging new dataset for car model recognition and viewpoint estimation that we introduce in this paper, the Biased-Cars dataset. Our results demonstrate that as the number of combinations seen during training increases, networks generalize better to unseen category-pose combinations, facilitated by an increase in the selectivity and invariance of individual neurons. We find that learning category and pose in separate networks compared to a shared one leads to an increase in such selectivity and invariance, as separate networks are not forced to preserve information about both category and pose. This enables separate networks to significantly outperform shared ones at predicting unseen category-pose combinations.

}, author = {Spandan Madan and Timothy Henry and Jamell Dozier and Helen Ho and Nishchal Bhandari and Tomotake Sasaki and Fredo Durand and Hanspeter Pfister and Xavier Boix} } @article {4478, title = {Do Neural Networks for Segmentation Understand Insideness?}, year = {2020}, month = {04/2020}, abstract = {

The insideness problem is an image segmentation modality that consists of determining which pixels are inside and outside a region. Deep Neural Networks (DNNs) excel in segmentation benchmarks, but it is unclear that they have the ability to solve the insideness problem as it requires evaluating long-range spatial dependencies. In this paper, the insideness problem is analyzed in isolation, without texture or semantic cues, such that other aspects of segmentation do not interfere in the analysis. We demonstrate that DNNs for segmentation with few units have sufficient complexity to solve insideness for any curve. Yet, such DNNs have severe problems to learn general solutions. Only recurrent networks trained with small images learn solutions that generalize well to almost any curve. Recurrent networks can decompose the evaluation of long-range dependencies into a sequence of local operations, and learning with small images alleviates the common difficulties of training recurrent networks with a large number of unrolling steps.

}, author = {Kimberly M. Villalobos and Vilim Štih and Amineh Ahmadinejad and Shobhita Sundaram and Jamell Dozier and Andrew Francl and Frederico Azevedo and Tomotake Sasaki and Xavier Boix} } @article {3962, title = {Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?}, year = {2018}, month = {12/2018}, abstract = {

THIS MEMO IS REPLACED BY CBMM MEMO 105

A key component of visual cognition is the understanding of spatial relationships among objects. Albeit effortless to our visual system, state-of-the-art artificial neural networks struggle to distinguish basic spatial relationships among elements in an image. As shown here, deep neural networks (DNNs) trained with hundreds of thousands of labeled examples cannot accurately distinguish whether pixels lie inside or outside 2D shapes, a problem that seems much simpler than image segmentation. In this paper, we sought to analyze the capability of ANN to solve such inside/outside problems using an analytical approach. We demonstrate that it is a mathematically tractable problem and that two previously proposed algorithms, namely the Ray-Intersection Method and the Coloring Method, achieve perfect accuracy when implemented in the form of DNNs.

}, author = {Kimberly M. Villalobos and Jamel Dozier and Vilim Stih and Andrew Francl and Frederico Azevedo and Tomaso Poggio and Tomotake Sasaki and Xavier Boix} } @conference {3893, title = {The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors}, booktitle = {workshop on "AI for Social Good", NIPS 2018}, year = {2018}, month = {11/2018}, address = {Montreal, Canada}, abstract = {

The digital information age has generated new outlets for content creators to publish so-called {\textquotedblleft}fake news{\textquotedblright}, a new form of propaganda that is intentionally designed to mislead the reader. With the widespread effects of the fast dissemination of fake news, efforts have been made to automate the process of fake news detection. A promising solution that has come up recently is to use machine learning to detect patterns in the news sources and articles, specifically deep neural networks, which have been successful in natural language processing. However, deep networks come with lack of transparency in the decision-making process, i.e. the {\textquotedblleft}black-box problem{\textquotedblright}, which obscures its reliability. In this paper, we open this {\textquotedblleft}black-box{\textquotedblright} and we show that the emergent representations from deep neural networks capture subtle but consistent differences in the language of fake and real news: signatures of exaggeration and other forms of rhetoric. Unlike previous work, we test the transferability of the learning process to novel news topics. Our results demonstrate the generalization capabilities of deep learning to detect fake news in novel subjects only from language patterns.

}, url = {http://hdl.handle.net/1721.1/120056}, author = {Nicole O{\textquoteright}Brien and Sophia Latessa and Georgios Evangelopoulos and Xavier Boix} } @article {3881, title = {Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results}, year = {2018}, month = {11/2018}, abstract = {

Deep neural networks have been shown to predict neural responses in higher visual cortex. The mapping from the model to a neuron in the brain occurs through a linear combination of many units in the model, leaving open the question of whether there also exists a correspondence at the level of individual neurons. Here we show that there exist many one-to-one mappings between single units in a deep neural network model and neurons in the brain. We show that this correspondence at the single- unit level is ubiquitous among state-of-the-art deep neural networks, and grows more pronounced for models with higher performance on a large-scale visual recognition task. Comparing matched populations{\textemdash}in the brain and in a model{\textemdash}we demonstrate a further correspondence at the level of the population code: stimulus category can be partially decoded from real neural responses using a classifier trained purely on a matched population of artificial units in a model. This provides a new point of investigation for phenomena which require fine-grained mappings between deep neural networks and the brain.

}, author = {Luke Arend and Yena Han and Martin Schrimpf and Pouya Bashivan and Kohitij Kar and Tomaso Poggio and James J. DiCarlo and Xavier Boix} } @conference {2491, title = {Active Video Summarization: Customized Summaries via On-line Interaction.}, booktitle = {AAAI Conference on Artificial Intelligence}, year = {2017}, author = {Garcia del Molino, A and X Boix and Lim, J. and Tan, A} } @article {2485, title = {Eccentricity Dependent Deep Neural Networks for Modeling Human Vision}, year = {2017}, author = {Gemma Roig and Francis Chen and X Boix and Tomaso Poggio} } @conference {2487, title = {Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision}, booktitle = {AAAI Spring Symposium Series, Science of Intelligence}, year = {2017}, abstract = {

Humans can recognize objects in a way that is invariant to scale, translation, and clutter. We use invariance theory as a conceptual basis, to computationally model this phenomenon. This theory discusses the role of eccentricity in human visual processing, and is a generalization of feedforward convolutional neural networks (CNNs). Our model explains some key psychophysical observations relating to invariant perception, while maintaining important similarities with biological neural architectures. To our knowledge, this work is the first to unify explanations of all three types of invariance, all while leveraging the power and neurological grounding of CNNs.

}, url = {https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15360}, author = {Francis Chen and Gemma Roig and Leyla Isik and X Boix and Tomaso Poggio} } @article {3266, title = {Theory of Deep Learning III: explaining the non-overfitting puzzle}, year = {2017}, month = {12/2017}, abstract = {

THIS MEMO IS REPLACED BY CBMM MEMO 90

A main puzzle of deep networks revolves around the absence of overfitting despite overparametrization and despite the large capacity demonstrated by zero training error on randomly labeled data. In this note, we show that the dynamical systems associated with gradient descent minimization of nonlinear networks behave near zero stable minima of the empirical error as gradient system in a quadratic potential with degenerate Hessian. The proposition is supported by theoretical and numerical results, under the assumption of stable minima of the gradient.

Our proposition provides the extension to deep networks of key properties of gradient descent methods for linear networks, that as, suggested in (1), can be the key to understand generalization. Gradient descent enforces a form of implicit regular- ization controlled by the number of iterations, and asymptotically converging to the minimum norm solution. This implies that there is usually an optimum early stopping that avoids overfitting of the loss (this is relevant mainly for regression). For classification, the asymptotic convergence to the minimum norm solution implies convergence to the maximum margin solution which guarantees good classification error for {\textquotedblleft}low noise{\textquotedblright} datasets.

The implied robustness to overparametrization has suggestive implications for the robustness of deep hierarchically local networks to variations of the architecture with respect to the curse of dimensionality.

}, author = {Tomaso Poggio and Keji Kawaguchi and Qianli Liao and Brando Miranda and Lorenzo Rosasco and Xavier Boix and Jack Hidary and Hrushikesh Mhaskar} } @article {1617, title = {Foveation-based Mechanisms Alleviate Adversarial Examples}, number = {044}, year = {2016}, month = {01/2016}, abstract = {

We show that adversarial examples,\ i.e.,\ the visually imperceptible perturbations that result in Convolutional Neural Networks (CNNs) fail, can be alleviated with a mechanism based on foveations---applying the CNN in different image regions. To see this, first, we report results in ImageNet that lead to a revision of the hypothesis that adversarial perturbations are a consequence of CNNs acting as a linear classifier: CNNs act locally linearly to changes in the image regions with objects recognized by the CNN, and in other regions the CNN may act non-linearly. Then, we corroborate that when the neural responses are linear, applying the foveation mechanism to the adversarial example tends to significantly reduce the effect of the perturbation. This is because, hypothetically, the CNNs for ImageNet are robust to changes of scale and translation of the object produced by the foveation, but this property does not generalize to transformations of the perturbation. As a result, the accuracy after a foveation is almost the same as the accuracy of the CNN without the adversarial perturbation, even if the adversarial perturbation is calculated taking into account a foveation.

}, author = {Luo, Yan and X Boix and Gemma Roig and Tomaso Poggio and Qi Zhao} }