Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning

TL;DR

ShrinkMatch enhances semi-supervised learning by adaptively shrinking class spaces for uncertain samples, enabling more effective utilization of unlabeled data and improving model confidence and accuracy.

Contribution

This work introduces ShrinkMatch, a novel method that dynamically reduces class confusion for uncertain samples, improving pseudo label quality in semi-supervised learning.

Findings

Achieves state-of-the-art results on benchmark datasets.

Effectively utilizes uncertain samples by shrinking class spaces.

Improves confidence calibration and overall accuracy.

Abstract

Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. To mitigate potentially incorrect pseudo labels, recent frameworks mostly set a fixed confidence threshold to discard uncertain samples. This practice ensures high-quality pseudo labels, but incurs a relatively low utilization of the whole unlabeled set. In this work, our key insight is that these uncertain samples can be turned into certain ones, as long as the confusion classes for the top-1 class are detected and removed. Invoked by this, we propose a novel method dubbed ShrinkMatch to learn uncertain samples. For each uncertain sample, it adaptively seeks a shrunk class space, which merely contains the original top-1 class, as well as remaining less likely classes. Since the confusion ones are removed in this space, the re-calculated top-1 confidence can satisfy the pre-defined threshold. We then impose a consistency regularization between a pair of strongly and weakly augmented samples in the shrunk space to strive for discriminative representations. Furthermore, considering the varied reliability among uncertain samples and the gradually improved model during training, we correspondingly design two reweighting principles for our uncertain loss. Our method exhibits impressive performance on widely adopted benchmarks. Code is available at https://github.com/LiheYoung/ShrinkMatch.