Vision Transformers Need Registers

Transformers have recently emerged as a powerful tool for learning visual representations. In this paper, we identify and characterize artifacts in feature maps of both supervised and self-supervised ViT networks. The artifacts correspond to high-norm tokens appearing during inference primarily in low-informative background areas of images, that are repurposed for internal computations. We propose a simple yet effective solution based on providing additional tokens to the input sequence of the Vision Transformer to fill that role. We show that this solution fixes that problem entirely for both supervised and self-supervised models, sets a new state of the art for self-supervised visual models on dense visual prediction tasks, enables object discovery methods with larger models, and most importantly leads to smoother feature maps and attention maps for downstream visual processing

Revisiting classifier two-sample tests

International audienceThe goal of two-sample tests is to assess whether two samples, $S P ∼ P n$ and $S Q ∼ Q m$ , are drawn from the same distribution. Perhaps intriguingly, one relatively unexplored method to build two-sample tests is the use of binary classifiers. In particular, construct a dataset by pairing the n examples in S P with a positive label, and by pairing the m examples in $S Q$ with a negative label. If the null hypothesis " $P = Q$ " is true, then the classification accuracy of a binary classifier on a held-out subset of this dataset should remain near chance-level. As we will show, such Classifier Two-Sample Tests (C2ST) learn a suitable representation of the data on the fly, return test statistics in interpretable units, have a simple null distribution, and their predictive uncertainty allow to interpret where P and Q differ. The goal of this paper is to establish the properties, performance, and uses of C2ST. First, we analyze their main theoretical properties. Second, we compare their performance against a variety of state-of-the-art alternatives. Third, we propose their use to evaluate the sample quality of generative models with intractable likelihoods, such as Generative Adversarial Networks (GANs). Fourth, we showcase the novel application of GANs together with C2ST for causal discovery

Co-training 2L2^L Submodels for Visual Recognition

We introduce submodel co-training, a regularization method related to co-training, self-distillation and stochastic depth. Given a neural network to be trained, for each sample we implicitly instantiate two altered networks, ``submodels'', with stochastic depth: we activate only a subset of the layers. Each network serves as a soft teacher to the other, by providing a loss that complements the regular loss provided by the one-hot label. Our approach, dubbed cosub, uses a single set of weights, and does not involve a pre-trained external model or temporal averaging. Experimentally, we show that submodel co-training is effective to train backbones for recognition tasks such as image classification and semantic segmentation. Our approach is compatible with multiple architectures, including RegNet, ViT, PiT, XCiT, Swin and ConvNext. Our training strategy improves their results in comparable settings. For instance, a ViT-B pretrained with cosub on ImageNet-21k obtains 87.4% top-1 acc. @448 on ImageNet-val

Is object localization for free? – Weakly-supervised learning with convolutional neural networks

International audienceSuccessful methods for visual object recognition typically rely on training datasets containing lots of richly annotatedimages. Detailed image annotation, e.g. by object bounding boxes, however, is both expensive and often subjective.We describe a weakly supervised convolutional neural network (CNN) for object classification that relies onlyon image-level labels, yet can learn from cluttered scenes containing multiple objects. We quantify its object classification and object location prediction performance on the Pascal VOC 2012 (20 object classes) and the much larger Microsoft COCO (80 object classes) datasets. We find that the network (i) outputs accurate image-level labels, (ii) predicts approximate locations (but not extents) of objects, and (iii) performs comparably to its fully-supervised counterparts using object bounding box annotation for training

Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks

Conference version of the paperInternational audienceConvolutional neural networks (CNN) have recently shown outstanding image classification performance in the large-scale visual recognition challenge (ILSVRC2012). The success of CNNs is attributed to their ability to learn rich mid-level image representations as opposed to hand-designed low-level features used in other image classification methods. Learning CNNs, however, amounts to estimating millions of parameters and requires a very large number of annotated image samples. This property currently prevents application of CNNs to problems with limited training data. In this work we show how image representations learned with CNNs on large-scale annotated datasets can be efficiently transferred to other visual recognition tasks with limited amount of training data. We design a method to reuse layers trained on the ImageNet dataset to compute mid-level image representation for images in the PASCAL VOC dataset. We show that despite differences in image statistics and tasks in the two datasets, the transferred representation leads to significantly improved results for object and action classification, outperforming the current state of the art on Pascal VOC 2007 and 2012 datasets. We also show promising results for object and action localization

Réseaux de neurones à convolution : vers moins de supervision pour la reconnaissance visuelle

Convolutional Neural Networks are flexible learning algorithms for computer vision that scale particularly well with the amount of data that is provided for training them. Although these methods had successful applications already in the ’90s, they were not used in visual recognition pipelines because of their lesser performance on realistic natural images. It is only after the amount of data and the computational power both reached a critical point that these algorithms revealed their potential during the ImageNet challenge of 2012, leading to a paradigm shift in visual recogntion. The first contribution of this thesis is a transfer learning setup with a Convolutional Neural Network for image classification. Using a pre-training procedure, we show that image representations learned in a network generalize to other recognition tasks, and their performance scales up with the amount of data used in pre-training. The second contribution of this thesis is a weakly supervised setup for image classification that can predict the location of objects in complex cluttered scenes, based on a dataset indicating only with the presence or absence of objects in training images. The third contribution of this thesis aims at finding possible paths for progress in unsupervised learning with neural networks. We study the recent trend of Generative Adversarial Networks and propose two-sample tests for evaluating models. We investigate possible links with concepts related to causality, and propose a two-sample test method for the task of causal discovery. Finally, building on a recent connection with optimal transport, we investigate what these generative algorithms are learning from unlabeled data.Les réseaux de neurones à convolution sont des algorithmes d’apprentissage flexibles qui tirent efficacement parti des importantes masses de données qui leur sont fournies pour l’entraînement. Malgré leur utilisation dans des applications industrielles dès les années 90, ces algorithmes n’ont pas été utilisés pour la reconnaissance d’image à cause de leurs faibles performances avec les images naturelles. C’est finalement grâce a l’apparition d’importantes quantités de données et de puissance de calcul que ces algorithmes ont pu révéler leur réel potentiel lors de la compétition ImageNet, menant à un changement de paradigme en reconnaissance d’image. La première contribution de cette thèse est une méthode de transfert d’apprentissage dans les réseaux à convolution pour la classification d’image. À l’aide d’une procédure de pré-entraînement, nous montrons que les représentations internes d’un réseau à convolution sont assez générales pour être utilisées sur d’autres tâches, et meilleures lorsque le pré-entraînement est réalisé avec plus de données. La deuxième contribution de cette thèse est un système faiblement supervisé pour la classification d’images, pouvant prédire la localisation des objets dans des scènes complexes, en utilisant, lors de l’entraînement, seulement l’indication de la présence ou l’absence des objets dans les images. La troisième contribution de cette thèse est une recherche de pistes de progression en apprentissage non-supervisé. Nous étudions l’algorithme récent des réseaux génératifs adversariaux et proposons l’utilisation d’un test statistique pour l’évaluation de ces modèles. Nous étudions ensuite les liens avec le problème de la causalité, et proposons un test statistique pour la découverte causale. Finalement, grâce a un lien établi récemment avec les problèmes de transport optimal, nous étudions ce que ces réseaux apprennent des données dans le cas non-supervisé

Réseaux de neurones à convolution : vers moins de supervision pour la reconnaissance visuelle

Convolutional Neural Networks are flexible learning algorithms for computer vision that scale particularly well with the amount of data that is provided for training them. Although these methods had successful applications already in the ’90s, they were not used in visual recognition pipelines because of their lesser performance on realistic natural images. It is only after the amount of data and the computational power both reached a critical point that these algorithms revealed their potential during the ImageNet challenge of 2012, leading to a paradigm shift in visual recogntion. The first contribution of this thesis is a transfer learning setup with a Convolutional Neural Network for image classification. Using a pre-training procedure, we show that image representations learned in a network generalize to other recognition tasks, and their performance scales up with the amount of data used in pre-training. The second contribution of this thesis is a weakly supervised setup for image classification that can predict the location of objects in complex cluttered scenes, based on a dataset indicating only with the presence or absence of objects in training images. The third contribution of this thesis aims at finding possible paths for progress in unsupervised learning with neural networks. We study the recent trend of Generative Adversarial Networks and propose two-sample tests for evaluating models. We investigate possible links with concepts related to causality, and propose a two-sample test method for the task of causal discovery. Finally, building on a recent connection with optimal transport, we investigate what these generative algorithms are learning from unlabeled data.Les réseaux de neurones à convolution sont des algorithmes d’apprentissage flexibles qui tirent efficacement parti des importantes masses de données qui leur sont fournies pour l’entraînement. Malgré leur utilisation dans des applications industrielles dès les années 90, ces algorithmes n’ont pas été utilisés pour la reconnaissance d’image à cause de leurs faibles performances avec les images naturelles. C’est finalement grâce a l’apparition d’importantes quantités de données et de puissance de calcul que ces algorithmes ont pu révéler leur réel potentiel lors de la compétition ImageNet, menant à un changement de paradigme en reconnaissance d’image. La première contribution de cette thèse est une méthode de transfert d’apprentissage dans les réseaux à convolution pour la classification d’image. À l’aide d’une procédure de pré-entraînement, nous montrons que les représentations internes d’un réseau à convolution sont assez générales pour être utilisées sur d’autres tâches, et meilleures lorsque le pré-entraînement est réalisé avec plus de données. La deuxième contribution de cette thèse est un système faiblement supervisé pour la classification d’images, pouvant prédire la localisation des objets dans des scènes complexes, en utilisant, lors de l’entraînement, seulement l’indication de la présence ou l’absence des objets dans les images. La troisième contribution de cette thèse est une recherche de pistes de progression en apprentissage non-supervisé. Nous étudions l’algorithme récent des réseaux génératifs adversariaux et proposons l’utilisation d’un test statistique pour l’évaluation de ces modèles. Nous étudions ensuite les liens avec le problème de la causalité, et proposons un test statistique pour la découverte causale. Finalement, grâce a un lien établi récemment avec les problèmes de transport optimal, nous étudions ce que ces réseaux apprennent des données dans le cas non-supervisé

Revisiting classifier two-sample tests

International audienceThe goal of two-sample tests is to assess whether two samples, $S P ∼ P n$ and $S Q ∼ Q m$ , are drawn from the same distribution. Perhaps intriguingly, one relatively unexplored method to build two-sample tests is the use of binary classifiers. In particular, construct a dataset by pairing the n examples in S P with a positive label, and by pairing the m examples in $S Q$ with a negative label. If the null hypothesis " $P = Q$ " is true, then the classification accuracy of a binary classifier on a held-out subset of this dataset should remain near chance-level. As we will show, such Classifier Two-Sample Tests (C2ST) learn a suitable representation of the data on the fly, return test statistics in interpretable units, have a simple null distribution, and their predictive uncertainty allow to interpret where P and Q differ. The goal of this paper is to establish the properties, performance, and uses of C2ST. First, we analyze their main theoretical properties. Second, we compare their performance against a variety of state-of-the-art alternatives. Third, we propose their use to evaluate the sample quality of generative models with intractable likelihoods, such as Generative Adversarial Networks (GANs). Fourth, we showcase the novel application of GANs together with C2ST for causal discovery

Revisiting classifier two-sample tests

International audienceThe goal of two-sample tests is to assess whether two samples, $S P ∼ P n$ and $S Q ∼ Q m$ , are drawn from the same distribution. Perhaps intriguingly, one relatively unexplored method to build two-sample tests is the use of binary classifiers. In particular, construct a dataset by pairing the n examples in S P with a positive label, and by pairing the m examples in $S Q$ with a negative label. If the null hypothesis " $P = Q$ " is true, then the classification accuracy of a binary classifier on a held-out subset of this dataset should remain near chance-level. As we will show, such Classifier Two-Sample Tests (C2ST) learn a suitable representation of the data on the fly, return test statistics in interpretable units, have a simple null distribution, and their predictive uncertainty allow to interpret where P and Q differ. The goal of this paper is to establish the properties, performance, and uses of C2ST. First, we analyze their main theoretical properties. Second, we compare their performance against a variety of state-of-the-art alternatives. Third, we propose their use to evaluate the sample quality of generative models with intractable likelihoods, such as Generative Adversarial Networks (GANs). Fourth, we showcase the novel application of GANs together with C2ST for causal discovery