Self-Supervised Visual Learning and Synthesis

Réalisation : 28 novembre 2019 Mise en ligne : 28 novembre 2019
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Computer vision has made impressive gains through the use of deep learning models, trained with large-scale labeled data. However, labels require expertise and curation and are expensive to collect. Can one discover useful visual representations without the use of explicitly curated labels? In this talk, I will present several case studies exploring the paradigm of self-supervised learning — using raw data as its own supervision. Several ways of defining objective functions in high-dimensional spaces will be discussed, including the use of General Adversarial Networks (GANs) to learn the objective function directly from the data. Applications of self-supervised learning will be presented, including colorization, on/off-screen source separation, image forensics, paired and unpaired image-to-image translation (aka pix2pix and cycleGAN), and curiosity-based exploration.

Lieu de réalisation
Sophia Antipolis
Langue :
CNRS - Centre National de la Recherche Scientifique (Publication), INRIA (Institut national de recherche en informatique et automatique) (Production), INRIA (Institut national de recherche en informatique et automatique) (Publication), UNS (Publication)
Conditions d'utilisation
Droit commun de la propriété intellectuelle
Citer cette ressource:
Inria. (2019, 28 novembre). Self-Supervised Visual Learning and Synthesis. [Vidéo]. Canal-U. (Consultée le 20 mai 2022)

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