Journée apprentissage appliqué aux données non-structurées (21 nov. 2019)
Journée apprentissage appliqué aux données non-structurées
21 novembre 2019
Lieu: Amphi Darwin, Institut Galilée, Université Paris 13
Cette journée est ouverte à tous, cependant l'inscription est obligatoire pour des questions d'organisation
Organisateurs: Anissa Mokraoui, Roberto Wolfler-Calvo
Evaluation de la fiabilité des algorithmes de "Machine Learning", Pierre DUHAMEL, L2S, Supelec
Deep Convolutional Auto-Encoders as Multiscale Inverse Problems, Tomás ANGLES, ENS ULM
Machine Learning driven Variable Frame-Rate in Video Broadcast Applications, Wassim HAMIDOUCHE, IETR, UMR CNRS 6164
Learning transforms for image/video compression, Aline ROUMY, INRIA Rennes
Enhancing HEVC spatial prediction by context-based learning, Li WANG, Telecom Paris Tech
Convex Functions and Neural Networks: a Case Study in Natural Language Processing, Joseph LEROUX, LIPN, Université Paris 13
Domain name recommendation based on Deep Neural Networks, Nistor GROZAVU, LIPN, Université Paris 13
9h00 Accueil des participants
9h15 Présentation FR MathSTIC et de la journée (Christophe FOUQUERE, Anissa MOKRAOUI et Roberto WOLFLER-CALVO)
9h30 Evaluation de la fiabilité des algorithmes de "Machine Learning"
Laboratoire des signaux et Signaux (L2S), Supelec
Les algorithmes d’apprentissage (et spécialement les algorithmes d’apprentissage profond (Deep Learning) nécessitent la disponibilité de grandes bases de données qui soient représentatives des situations dans lesquelles ils seront utilisés. Cette représentativité peut être mise en cause pas divers phénomènes, tels que la suradaptation (overfitting), l’évolution des statistiques des signaux d’intérêt, etc... il est donc très important d’évaluer si les sorties de tels algorithmes sont fiables.
Un tel objectif pourrait avoir de nombreuses applications, et permettrait de répondre à des questions comme
1- le réseau a été entrainé et est utilisé sur de nouvelles entrées. Si les statistiques de ces entrées évoluent, quand est-il nécessaire de recommencer l’entrainement ?
2- peut-on obtenir une mesure de précision des sorties de l’algorithme, de telle sorte à “pondérer” les décisions correspondantes ?
3- peut-on détecter des ”outliers” ?
Même si nous en pouvons répondre à toutes ces questions, l’exposé expliquera tout d’abord comment poser le problème, en faisant un rapide état de l’art. Puis l’exposé se concentrera sur un approche “boite noire”, dont nous expliquerons la large utilité, et pour laquelle nous proposerons une première série de méthodes avec les simulations correspondantes.
10h15 Deep Convolutional Auto-Encoders as Multiscale Inverse Problems
ENS de l'université PSL (ENS ULM)
Constructing generative models of high-dimensional random processes is a central problem in Machine Learning, Image Processing, and Mathematics. Auto-Encoders based on Deep Convolutional Networks seem to solve this problem by taking advantage of strong regularities in natural signals, but the specific mathematical mechanisms to exploit these regularities are not well-understood. We present a model of Deep Convolutional Auto-Encoders for which we can explicitly state the mechanisms used to obtain a generative model. We propose a multiscale encoder based on a scattering transform that Gaussianizes the random process using a central limit theorem and exploits spatial regularities at different scales as well as the dependencies across scales found in natural signals. Furthermore, the encoder is structured such that the decoder inverts a code by solving a sequence of linear inverse problems at different scales. These inverse problems are stabilized using sparsity by learning a sequence of convolutional dictionaries, one per scale. Our model provides insights and potential explanations for the properties of generated signals obtained with Deep Convolutional Auto-Encoders.
11h-11h15 : Pause café
11h15 Machine Learning driven Variable Frame-Rate in Video Broadcast Applications
Institut d'Électronique et de Télécommunications de Rennes (IETR, UMR CNRS 6164)
The Digital Video Broadcasting (DVB) has proposed to introduce the Ultra-High Definition services in three phases: UHD-1 phase 1, UHD-1 phase 2 and UHD-2. The UHD-1 phase 2 specification includes several new features such as High Dynamic Range (HDR) and High Frame-Rate (HFR). It has been shown in several studies that HFR (+100 fps) enhances the perceptual quality and that this quality enhancement is content-dependent. On the other hand, HFR brings several challenges to the transmission chain including codec complexity increase and bit-rate overhead, which may delay or even prevent its deployment in the broadcast echo-system. In this talk, I will present our proposed Variable Frame Rate (VFR) solution to determine the minimum (critical) frame-rate that preserves the perceived video quality of the HFR video. The frame-rate determination is modeled as a 3-class classification problem which consists in dynamically and locally selecting one frame-rate among
three: 30, 60 and 120 frames per second. Two random forests classifiers are trained with a ground truth carefully built by experts for this purpose. The subjective results conducted on ten HFR video contents (not included in the training set) clearly show the efficiency of the proposed solution enabling to locally determine the lowest possible frame-rate while preserving the quality of the HFR content. Moreover, our VFR solution enables significant bit-rate savings and complexity reductions at both the encoder and decoder sides.
12h00 Learning transforms for image/video compression
Learning transforms is a key ingredient in image/video compression, which allows to adapt to the statistics of the image and more precisely to its local dependencies. In this talk, two types of adaptation will be presented. First, deep learning techniques for compression of 2D images is considered. We have in particular addressed the problem of learning transforms that would be optimal in terms of energy compaction. This unsupervised learning problem has been addressed by proposing auto-encoders with a rate-distortion cost function, and not only a distortion criterion, as is classically done in machine learning. The proposed neural network can work efficiently at any coding rate using an adaptation to the quantization noise during training.
In the second part of the talk, a transform is learned on the sphere to compress 360-degree images. Omni-directional images are characterized by their high resolution (usually 8K) and therefore require high compression efficiency. Existing methods project the spherical content onto one or multiple planes and process the mapped content with classical 2D video coding algorithms. However, this projection induces sub-optimality. Indeed, after projection, the statistical properties of the pixels are modified, the connectivity between neighboring pixels on the sphere might be lost, and finally, the sampling is not uniform. Therefore, we propose to process uniformly distributed pixels directly on the sphere to achieve high compression efficiency. In particular, a scanning order and a prediction scheme are proposed to exploit, directly on the sphere, the statistical dependencies between the pixels. A transform is also learned to exploit local dependencies while taking into account the 3D geometry.
12h45-14h15 : Pause déjeuner
14h15 Enhancing HEVC spatial prediction by context-based learning
Telecom Paris Tech (Groupe Multimédia)
Deep generative models have been recently employed to compress images, image residuals or to predict image regions. Based on the observation that state-of-the-art spatial prediction is highly optimized from a rate-distortion point of view, we study how learning-based approaches might be used to further enhance this prediction. To this end, we propose an encoder-decoder convolutional network able to reduce the energy of the residuals of HEVC intra prediction, by leveraging the available context of previously decoded neighboring blocks. The proposed context-based prediction enhancement (CBPE) scheme enables to reduce the mean square error of HEVC prediction by 25% on average, without any additional signaling cost in the bitstream.
15h00 Convex Functions and Neural Networks: a Case Study in Natural Language Processing
LIPN, Université Paris 13
In recent years, neural networks (NN) have become ubiquitous in Natural Language Processing (NLP), with tremendous progress made in Machine Translation, Summarization etc... They offer a rich parameterization of score functions and almost unbounded context. On the other hand, for tasks enforcing a strong a priori on the output structure, such as parsing which requires that output are arborescences, NNs are mainly used as better replacements for old technology such as feature extractors (recurrent networks) and classifiers (multi-layered perceptrons), whose scores are then passed to combinatorial algorithms that generate discrete structures. One of the problems with this approach is that the combination of the parts of the structures must be fixed in advance by hand (think for instance of a maximum spanning arborescence problem where the objective function is fixed as the sum of the arcs the arborescence). In this talk I will present some preliminary work which try to depart from this approach and learn the combination function itself, while remaining efficient. We use Structured Prediction Energy Networks (SPENs) and enforce convexity, in some of the input, of the function computed by the neural networks so we can rely on efficient optimization methods to perform learning and decoding.
15h45 Domain name recommendation based on Deep Neural Networks
LIPN, Université Paris 13
Recommender system has been an effective key solution to guide users in a personalized way for discovering the domain name they might be interested in, from a large space of possible suggestion. They have become fundamental applications that provides to users the best domain name that meet their needs and preferences. In this talk, I will focus on the use of Deep Learning methods for domain name suggestion based on the research made by the users. We proposed three approaches to recommend domain name based on neural networks. The first one consists on discovering the similarity between the vocabularies of domain name, while the second one is finding the relevant Top Level Domain (TLD) corresponding to the context of the domain name. The third method consists on training a Multi-Layer Perceptron and an LSTM (Long short-term memory) neural network on a dictionary of the most commonly used n-grams in order to extract words from a string of characters not separated by spaces. To predict the TLDS, the sales history data is used and word immersion models pre-trained on a wide range of topics corpus as well as on n-gram dictionaries.
16h30 Clôture de la journée