Barely-Supervised Learning: Semi-Supervised Learning with very few labeled images

Understand

This paper tackles the problem of semi-supervised learning when the set of labeled samples is limited to a small number of images per class, typically less than 10, problem that we refer to as barely-supervised learning.

• We analyze in depth the behavior of a state-of-the-art semi-supervised method, FixMatch, which relies on a weakly-augmented version of an image to obtain supervision signal for a more strongly-augmented version.
• We show that it frequently fails in barely-supervised scenarios, due to a lack of training signal when no pseudo-label can be predicted with high confidence.
• We propose a method to leverage self-supervised methods that provides training signal in the absence of confident pseudo-labels.