Title: Self-Supervised Learning — Can One Really Do Away with Data Labeling?

Abstract: Self-Supervised Learning is a hot topic, spurred by two landmark papers from giants of AI and Machine Learning — Geoff Hinton and Yann LeCun, respectively. The implied claim is that one can train a model without using any labels and later develop a classifier that performs well using only very limited labels. We will discuss this exciting area, along with the current state of the narrative, empirical findings and theoretical research. This is joint work with XY Han (University of Chicago) and Vardan Papyan (University of Toronto).