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- Date de réalisation : 28 Novembre 2019
- Lieu de réalisation : Sophia Antipolis
- Durée du programme : 79 min
- Classification Dewey : Infographie, Processing modes--computer science--multimedia-systems programs, . . ., machine learning
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- Catégorie : Conférences
- Niveau : niveau Master (LMD), niveau Doctorat (LMD), Recherche
- Disciplines : Informatique
- Collections : Colloquium Jacques Morgenstern : recherches en STIC - nouveaux thèmes scientifiques, nouveaux domaines d’application, et enjeux
- ficheLom : Voir la fiche LOM
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- Auteur(s) : Efros Alexei A.
- producteur : INRIA (Institut national de recherche en informatique et automatique)
- Editeur : INRIA (Institut national de recherche en informatique et automatique) , CNRS - Centre National de la Recherche Scientifique , UNS
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Self-Supervised Visual Learning and Synthesis
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.
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