Welcome on the website of the Paris Seminar on the Mathematics of Imaging.
The goal of this seminar is to cover the fields of the mathematics of imaging in a very wide sense (including for instance signal processing, image processing, computer graphics, computer vision, various applications and connexion with statistics and machine learning). It is open to everyone. It takes place the first Thursday of each month at IHP, from 14:00 to 16:00. Each seminar is composed of two presentations.
You can also subscribe to the mailing list of the seminar.
14 juin 2018, 14h-15h, room 314.
Pablo Musé (Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay)
Title: OLÉ, Orthogonal Low-rank Embedding, A Novel Approach for Deep Metric Learning
Abstract: Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are common tools for image classification. Yet, this does not naturally enforce intra-class similarity nor inter-class margin of the learned deep representations. To simultaneously achieve these two goals, different solutions have been proposed in the literature, such as the pairwise or triplet losses. However, such solutions carry the extra task of selecting pairs or triplets, and the extra computational burden of computing and learning for many combinations of them. In this talk we present a plug-and-play loss term for deep networks that explicitly reduces intra-class variance and enforces inter-class margin simultaneously, in a simple geometric manner. For each class, the deep features are collapsed into a learned linear subspace, or union of them, and inter-class subspaces are pushed to be as orthogonal as possible. Our proposed Orthogonal Low-rank Embedding does not require carefully crafting pairs or triplets of samples for training, and works standalone as a classification loss. Because of the improved margin between features of different classes, the resulting deep networks generalize better, are more discriminative and more robust. This is a joint work with José Lezama, Qiang Qiu and Guillermo Sapiro
14 juin 2018, 15h-16h, room 314.
Paul Escande (Johns Hopkins University)
Title: Multi-scale Decomposition of Transformations (MUSCADET)
Abstract: In many applications, transformations between two domains are defined through point-wise mappings. These functions can be costly to store and compute, but also hard to interpret in a geometric fashion. In this work, we propose a way to overcome these difficulties. The main idea is a novel multi-scale decomposition of complex transformations into a cascade of elementary, user-specified, transformations. This methods allows to (i) Construct efficient approximations for elements of large spaces of complex transformations using simple understandable blocks, (ii) Use transformations to measure similarities between complex objects, (iii) Deal with invariance under certain transformations, (iv) Perform statistical inference tasks on sets of transformations. We will describe the method as well as provide theoretical guarantees on the quality of the multi-scale approximations. Then we will present some numerical experiments that show its computational efficiency.
8 novembre 2018, 14h-15h, room 314.
Rémi Gribonval (INRIA, Panama project-team)
Bienvenu sur le site du Séminaire Parisien des Mathématiques Appliquées à l’Imagerie.
Le but de ce séminaire est de couvrir le domaine des mathématiques de l’imagerie. Il est ouvert à tous. Le séminaire a lieu le premier jeudi de chaque mois à l’IHP, de 14h à 16h. Chaque séance est composée de deux exposés.
Vous pouvez également vous abonner ou désabonner à la liste de diffusion du séminaire.