
#86 - Prof. YANN LECUN and Dr. RANDALL BALESTRIERO - SSL, Data Augmentation, Reward isn't enough [NEURIPS2022]
Machine Learning Street Talk (MLST)
Advancements in Self-Supervised Learning
This chapter delves into self-supervised learning (SSL) and its relationship with spectral embedding methods, addressing challenges in selecting loss functions for diverse datasets. It introduces the innovative NNClear method for automating augmentations and discusses the evolution of contrastive and non-contrastive learning approaches, highlighting contributions from pioneers like Jeff Hinton and Yann. The speakers emphasize the importance of contextual understanding in choosing learning methods to optimize representation and efficiency.
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