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Self-supervised learning of Split Invariant Equivariant representations
Quentin Garrido · Laurent Najman · Yann LeCun

Wed Jul 26 05:00 PM -- 06:30 PM (PDT) @ Exhibit Hall 1 #136

Recent progress has been made towards learning invariant or equivariant representations with self-supervised learning. While invariant methods are evaluated on large scale datasets, equivariant ones are evaluated in smaller, more controlled, settings. We aim at bridging the gap between the two in order to learn more diverse representations that are suitable for a wide range of tasks. We start by introducing a dataset called 3DIEBench, consisting of renderings from 3D models over 55 classes and more than 2.5 million images where we have full control on the transformations applied to the objects. We further introduce a predictor architecture based on hypernetworks to learn equivariant representations with no possible collapse to invariance. We introduce SIE (Split Invariant-Equivariant) which combines the hypernetwork-based predictor with representations split in two parts, one invariant, the other equivariant, to learn richer representations. We demonstrate significant performance gains over existing methods on equivariance related tasks from both a qualitative and quantitative point of view. We further analyze our introduced predictor and show how it steers the learned latent space. We hope that both our introduced dataset and approach will enable learning richer representations without supervision in more complex scenarios. Code and data are available at https://github.com/garridoq/SIE.

Author Information

Quentin Garrido (Meta AI - FAIR, Université Gustave Eiffel)
Laurent Najman (Université Gustave Eiffel - ESIEE Paris)
Laurent Najman

Laurent Najman received the Habilitation à Diriger les Recherches in 2006 from the University of Marne-la-Vallée, a Ph.D. in applied mathematics from Paris-Dauphine University in 1994 with the highest honor (Félicitations du Jury) and an “Ingénieur” degree from the Ecole des Mines de Paris in 1991. After earning his engineering degree, he worked in the Central Research Laboratories of Thomson-CSF for three years, working on some problems of infrared image segmentation using mathematical morphology. He then joined a start-up company named Animation Science in 1995, as director of research and development. The technology of particle systems for computer graphics and scientific visualization, developed by the company under his technical leadership received several awards, including the “European Information Technology Prize 1997” awarded by the European Commission (Esprit program) and by the European Council for Applied Science and Engineering and the “Hottest Products of the Year 1996” awarded by the Computer Graphics World journal. In 1998, he joined OCÉ Print Logic Technologies, as senior scientist. He worked there on various problem of image analysis dedicated to scanning and printing. In 2002, he joined the Computer Sciences Department of ESIEE, Paris, where he is full professor and the leader of the A3SI team of the Laboratoire d’Informatique Gaspard Monge, Université Gustave Eiffel. His current research interests include the study of the topology of discrete structures (such as graphs, hierarchies, and simplicial complexes), using discrete mathematical morphology and discrete optimization.

Yann LeCun (New York University)

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