Training Discriminative Models to Evaluate Generative Ones

Generative models are known to be difficult to assess. Recent works, especially on generative adversarial networks (GANs), produce good visual samples of varied categories of images. However, the validation of their quality is still difficult to define and there is no existing agreement on the best evaluation process. This paper aims at making a step toward an objective evaluation process for generative models. It presents a new method to assess a trained generative model by evaluating the test accuracy of a classifier trained with generated data. The test set is composed of real images. Therefore, The classifier accuracy is used as a proxy to evaluate if the generative model fit the true data distribution. By comparing results with different generated datasets we are able to classify and compare generative models. The motivation of this approach is also to evaluate if generative models can help discriminative neural networks to learn, i.e., measure if training on generated data is able to make a model successful at testing on real settings. Our experiments compare different generators from the Variational Auto-Encoders (VAE) and Generative Adversarial Network (GAN) frameworks on MNIST and fashion MNIST datasets. Our results show that none of the generative models is able to replace completely true data to train a discriminative model. But they also show that the initial GAN and WGAN are the best choices to generate on MNIST database (Modified National Institute of Standards and Technology database) and fashion MNIST database

State Representation Learning for Control: An Overview

Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. The representation is learned to capture the variation in the environment generated by the agent's actions; this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension characteristic of the representation helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research

Exploration Strategies for Incremental Learning of Object-Based Visual Saliency

International audienceSearching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available. We describe a method to learn such an object-based visual saliency in an intrinsically motivated way using an environment exploration mechanism. We first define saliency in a geometrical manner and use this definition to discover salient elements given an attentive but costly observation of the environment. These elements are used to train a fast classifier that predicts salient objects given large-scale visual features. In order to get a better and faster learning, we use intrinsic motivation to drive our observation selection, based on uncertainty and novelty detection. Our approach has been tested on RGB-D images, is real-time, and outperforms several state-of-the-art methods in the case of indoor object detection

Apprentissage incrémental de la saillance visuelle pour des applications robotique

National audienceNous proposons une méthode d'apprentissage incrémental de la saillance visuelle par un mécanisme d'exploration de l'environnement. Partant d'une définition géométrique de la saillance des objets, notre système observe de façon attentive et ciblée son environnement, jusqu'à découvrir des éléments saillants. Un classifieur permet alors d'apprendre les caractéristiques visuelles correspondantes afin de pouvoir ensuite prédire rapidement les positions des objets sans analyse géométrique. Notre approche a été testée sur des images RGBD, fonctionne en temps réel et dépasse plusieurs méthodes de l'état de l'art sur le contexte particulier de la détection d'objets en intérieur

On the Use of Intrinsic Motivation for Visual Saliency Learning

International audienceThe use of intrinsic motivation for the task of learning sensori-motor properties has received a lot of attention over the last few years, but only little work has been provided toward using intrinsic motivation for the task of learning visual signals. In this paper, we propose to apply the main ideas of the Intelligent Adaptive Curiosity (IAC) for the task of visual saliency learning. We here present RL-IAC, an adapted version of IAC that uses reinforcement learning to deal with time consuming displacements while actively learning saliency based on local learning progress. We also introduce the use of a backward evaluation to deal with a learner that is shared between several regions. We demonstrate the good performance of RL-IAC compared to other exploration techniques, and we discuss the performance of other intrinsic motivation sources instead of learning progress in our problem

RL-IAC: An Exploration Policy for Online Saliency Learning on an Autonomous Mobile Robot

International audienceIn the context of visual object search and localization, saliency maps provide an efficient way to find object candidates in images. Unlike most approaches, we propose a way to learn saliency maps directly on a robot, by exploring the environment, discovering salient objects using geometric cues, and learning their visual aspects. More importantly, we provide an autonomous exploration strategy able to drive the robot for the task of learning saliency. For that, we describe the Reinforcement Learning-Intelligent Adaptive Curiosity algorithm (RL-IAC), a mechanism based on IAC (Intelligent Adaptive Curiosity) able to guide the robot through areas of the space where learning progress is high, while minimizing the time spent to move in its environment without learning. We demonstrate first that our saliency approach is an efficient tool to generate relevant object boxes proposal in the input image and significantly outperforms state-of-the-art algorithms. Second, we show that RL-IAC can drastically decrease the required time for learning saliency compared to random exploration

Exploring to learn visual saliency: The RL-IAC approach

International audienceThe problem of object localization and recognition on autonomous mobile robots is still an active topic. In this context, we tackle the problem of learning a model of visual saliency directly on a robot. This model, learned and improved on-the-fly during the robot's exploration provides an efficient tool for localizing relevant objects within their environment. The proposed approach includes two intertwined components. On the one hand, we describe a method for learning and incrementally updating a model of visual saliency from a depth-based object detector. This model of saliency can also be exploited to produce bounding box proposals around objects of interest. On the other hand, we investigate an autonomous exploration technique to efficiently learn such a saliency model. The proposed exploration, called Reinforcement Learning-Intelligent Adaptive Curiosity (RL-IAC) is able to drive the robot's exploration so that samples selected by the robot are likely to improve the current model of saliency. We then demonstrate that such a saliency model learned directly on a robot outperforms several state-of-the-art saliency techniques, and that RL-IAC can drastically decrease the required time for learning a reliable saliency model

Apport de la dimension temporelle aux traitements de veille infrarouge marine

Un système de veille infrarouge marine a pour fonction de restituer de manière entièrement automatique à son opérateur un état des menaces potentielles dans un rayon d'une dizaine de kilomètres. La problématique de cette thèse est de mesurer l'apport d'un traitement spatio-temporel au niveau du module de détection. Les travaux de thèse sont constitués de trois volets. Le premier concerne l'apport de la dimension temporelle pour le blanchiment des séquences d'images, préalablement à la détection. Les contributions à ce domaine ont consisté d'une part en un état de l'at interdisciplinaire, d'autre part en l'adaptation puis l'évaluation pour cette aplication des algorithmes les plus prometteurs. Le second est l'étude des algorithmes d'estimation d'un champ dense de déplacement, afin de stabiliser la séquence d'images, puis d'estimer la présence de cibles par un critère cinématique par rapport à l'environnement. Néanmoins, les algorithmes classiques de ce domaine ne sont pas adaptés à un fond dont l'intensité fluctue très rapidement comme les fonds de mer. Le troisième volet est l'étude des algorithmes de super-résolution, qui sont une alternative à à la conception opto-électronique pour augmenter la résolution d'une image. La question fondamentale est le lien entre l'augmentation de la résolution et l'augmentation des performances. En particulier, la question du bruit en super-résolution est cruciale, et constitue une part importante du travail effectué durant cette thèse.The function of a navaI InfraRed Search and Track system is the fully automated display of the potential threats around the ship to the operator. The aim of this PhD thesis is the measurement of the interest of a spatio-temporal processing for the detection module. The work presented here consists in three parts. The first axis concerns the use of the temporal dimension for image sequences whitening, before the detection algorthm. The contributions in this field consist in an interdisciplinary state-of-the-art, followed by the adaptation and evaluation of the most promising algorithms. The second axis is the study of dense displacement field estimation algorithms, for the image sequence stabilization, then for the target detection, using a kinematic criterion. Unfortunately, the classical displacement estimation algorithms do not work properly for very quickly varying conditions like marine imagery. The third axis is the study of super-resolution algorithms, which are an alternative to opto-electronic conception for the inprovement of the device resolution. The fundamental question is the link between resolution increase and detection performances increase. Especially, the queston of noise in super-resolution is of utmost importance, and teherfore constitute a large part of the work presented here.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Qualification and Evaluation of Performances

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Environment Exploration for Object-Based Visual Saliency Learning

International audienceSearching for objects in an indoor environment can be drastically improved if a task-specific visual saliency is available. We describe a method to incrementally learn such an object-based visual saliency directly on a robot, using an environment exploration mechanism. We first define saliency based on a geometrical criterion and use this definition to segment salient elements given an attentive but costly and restrictive observation of the environment. These elements are used to train a fast classifier that predicts salient objects given large-scale visual features. In order to get a better and faster learning, we use an exploration strategy based on intrinsic motivation to drive our attentive observation. Our approach has been tested on a robot in our lab as well as on publicly available RGB-D images sequences. We demonstrate that the approach outperforms several state-of-the-art methods in the case of indoor object detection and that the exploration strategy can drastically decrease the time required for learning saliency