Informations pratiques

Description de la ressource

Résumé

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.

"Domaine(s)" et indice(s) Dewey

• Infographie (006.6)
• Processing modes--computer science--multimedia-systems programs, . . . (006.787)
• machine learning (006.31)

Domaine(s)

Intervenants

Édition

• INRIA (Institut national de recherche en informatique et automatique)

Diffusion

Cette ressource vous est proposée par :

• Cette ressource fait partie de
  • Colloquium Jacques Morgenstern : recherches en STIC - nouveaux thèmes scientifiques, nouveaux domaines d’application, et enjeux