  Self-supervised learning (SSL) has emerged as a powerful framework to learn
representations from raw data without supervision. Yet in practice, engineers
face issues such as instability in tuning optimizers and collapse of
representations during training. Such challenges motivate the need for a theory
to shed light on the complex interplay between the choice of data augmentation,
network architecture, and training algorithm. We study such an interplay with a
precise analysis of generalization performance on both pretraining and
downstream tasks in a theory friendly setup, and highlight several insights for
SSL practitioners that arise from our theory.
