Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss

By standard generalization bounds, these accuracy guarantees also hold when minimizing the training contrastive loss. Empirically, the features learned by our objective can match or outperform several strong baselines on benchmark vision datasets.