2019
Learning Representations by Maximizing Mutual Information Across Views
NeurIPS 2019poster
We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g.…