On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?

no code implementations • 16 Nov 2020 • Yao-Chun Chan, Mingchen Li, Samet Oymak

In parallel, recent developments in self-supervised and semi-supervised learning (S4L) provide powerful techniques, based on data-augmentation, contrastive learning, and self-training, that enable superior utilization of unlabeled data which led to a significant reduction in required labeling in the standard machine learning benchmarks.

Active Learning Contrastive Learning +1