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Michael Rabbat

Membre industriel associé
Chercheur scientifique, AMI Labs
Sujets de recherche
Apprentissage de représentations
Optimisation
Systèmes distribués
LinkedIn

Biographie

Mike Rabbat est membre affilié de Mila – Institut québécois d’intelligence artificielle et directeur de la recherche scientifique au sein de l'équipe FAIR (Fundamental AI Research) de Meta. Ses recherches portent sur l'apprentissage efficace et robuste des représentations, en particulier l'apprentissage autosupervisé. Il s'intéresse également à l'optimisation pour un apprentissage efficace des modèles.

Publications

Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
Mahmoud Assran
Quentin Duval
Ishan Misra
Piotr Bojanowski
Pascal Vincent
Michael Rabbat
Yann Lecun
Nicolas Ballas
This paper demonstrates an approach for learning highly semantic image representations without relying on hand-crafted data-augmentations. W… (voir plus)e introduce the Image-based Joint-Embedding Predictive Architecture (I-JEPA), a non-generative approach for self-supervised learning from images. The idea behind I-JEPA is simple: from a single context block, predict the representations of various target blocks in the same image. A core design choice to guide I-JEPA towards producing semantic representations is the masking strategy; specifically, it is crucial to (a) sample target blocks with sufficiently large scale (semantic), and to (b) use a sufficiently informative (spatially distributed) context block. Empirically, when combined with Vision Transformers, we find I-JEPA to be highly scalable. For instance, we train a ViT-Huge/14 on ImageNet using 16 A100 GPUs in under 72 hours to achieve strong downstream performance across a wide range of tasks, from linear classification to object counting and depth prediction.
2023-06-16
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (publié)
doi.org
arxiv.org