Colloquium Jacques Morgenstern
Self-Supervised Visual Learning and Synthesis
Alexei A. Efros, professeur à Berkeley, présentera le 28 novembre 2019 à 11h ses travaux lors du prochain colloquium J. Morgenstern : "Self-Supervised Visual Learning and Synthesis"
- Date : 28/11/2019
- Lieu : Inria, Sophia Antipolis, Amphithéâtre, Bâtiment Kahn
- Intervenant(s) : Alexei A. Efros , UC Berkeley
- Organisateur(s) : Comité Colloquium Jacques Morgenstern
Titre, résumé et présentation sont exclusivement en anglais.
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.