Nonparametric Transient Classification using Adaptive Wavelets

Classifying transients based on multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classifier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a characterization of functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classifier on the wavelet coefficients that handles both the heteroscedasticity of the data in addition to the potential non-representativity of the training set. The ranked classifier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, BAGIDIS is nonparametric so it can be used for blind searches for new objects. We demonstrate the effectiveness of our ranked wavelet classifier against the well-tested Supernova Photometric Classification Challenge dataset in which the challenge is to correctly classify light curves as Type Ia or non-Ia supernovae. We train our ranked probability classifier on the spectroscopically-confirmed subsample (which is not representative) and show that it gives good results for all supernova with observed light curve timespans greater than 100 days (roughly 55% of the dataset). For such data, we obtain a Ia efficiency of 80.5% and a purity of 82.4% yielding a highly competitive score of 0.49 whilst implementing a truly "model-blind" approach to supernova classification. Consequently this approach may be particularly suitable for the classification of astronomical transients in the era of large synoptic sky surveys.Comment: 14 pages, 8 figures. Published in MNRA

Toward robust and efficient physically-based rendering

Le rendu fondé sur la physique est utilisé pour le design, l'illustration ou l'animation par ordinateur. Ce type de rendu produit des images photo-réalistes en résolvant les équations qui décrivent le transport de la lumière dans une scène. Bien que ces équations soient connues depuis longtemps, et qu'un grand nombre d'algorithmes aient été développés pour les résoudre, il n'en existe pas qui puisse gérer de manière efficace toutes les scènes possibles. Plutôt qu'essayer de développer un nouvel algorithme de simulation d'éclairage, nous proposons d'améliorer la robustesse de la plupart des méthodes utilisées à ce jour et/ou qui sont amenées à être développées dans les années à venir. Nous faisons cela en commençant par identifier les sources de non-robustesse dans un moteur de rendu basé sur la physique, puis en développant des méthodes permettant de minimiser leur impact. Le résultat de ce travail est un ensemble de méthodes utilisant différents outils mathématiques et algorithmiques, chacune de ces méthodes visant à améliorer une partie spécifique d'un moteur de rendu. Nous examinons aussi comment les architectures matérielles actuelles peuvent être utilisées à leur maximum afin d'obtenir des algorithmes plus rapides, sans ajouter d'approximations. Bien que les contributions présentées dans cette thèse aient vocation à être combinées, chacune d'entre elles peut être utilisée seule : elles sont techniquement indépendantes les unes des autres.Physically-based rendering is used for design, illustration or computer animation. It consists in producing photorealistic images by solving the equations which describe how light travels in a scene. Although these equations have been known for a long time and many algorithms for light simulation have been developed, no algorithm exists to solve them efficiently for any scene. Instead of trying to develop a new algorithm devoted to light simulation, we propose to enhance the robustness of most methods used nowadays and/or which can be developed in the years to come. We do this by first identifying the sources of non-robustness in a physically-based rendering engine, and then addressing them by specific algorithms. The result is a set of methods based on different mathematical or algorithmic methods, each aiming at improving a different part of a rendering engine. We also investigate how the current hardware architectures can be used at their maximum to produce more efficient algorithms, without adding approximations. Although the contributions presented in this dissertation are meant to be combined, each of them can be used in a standalone way: they have been designed to be internally independent of each other

Frugal Reinforcement-based Active Learning

Most of the existing learning models, particularly deep neural networks, are reliant on large datasets whose hand-labeling is expensive and time demanding. A current trend is to make the learning of these models frugal and less dependent on large collections of labeled data. Among the existing solutions, deep active learning is currently witnessing a major interest and its purpose is to train deep networks using as few labeled samples as possible. However, the success of active learning is highly dependent on how critical are these samples when training models. In this paper, we devise a novel active learning approach for label-efficient training. The proposed method is iterative and aims at minimizing a constrained objective function that mixes diversity, representativity and uncertainty criteria. The proposed approach is probabilistic and unifies all these criteria in a single objective function whose solution models the probability of relevance of samples (i.e., how critical) when learning a decision function. We also introduce a novel weighting mechanism based on reinforcement learning, which adaptively balances these criteria at each training iteration, using a particular stateless Q-learning model. Extensive experiments conducted on staple image classification data, including Object-DOTA, show the effectiveness of our proposed model w.r.t. several baselines including random, uncertainty and flat as well as other work.Comment: arXiv admin note: text overlap with arXiv:2203.1156

Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection

Satellite image change detection aims at finding occurrences of targeted changes in a given scene taken at different instants. This task is highly challenging due to the acquisition conditions and also to the subjectivity of changes. In this paper, we investigate satellite image change detection using active learning. Our method is interactive and relies on a question and answer model which asks the oracle (user) questions about the most informative display (dubbed as virtual exemplars), and according to the user's responses, updates change detections. The main contribution of our method consists in a novel adversarial model that allows frugally probing the oracle with only the most representative, diverse and uncertain virtual exemplars. The latter are learned to challenge the most the trained change decision criteria which ultimately leads to a better re-estimate of these criteria in the following iterations of active learning. Conducted experiments show the out-performance of our proposed adversarial display model against other display strategies as well as the related work.Comment: arXiv admin note: substantial text overlap with arXiv:2203.1155