Enhancing Sample Utilization through Sample Adaptive Augmentation in Semi-Supervised Learning

TL;DR

This paper introduces Sample Adaptive Augmentation (SAA), a simple and lightweight method that selectively diversifies augmentation for naive samples in semi-supervised learning, significantly boosting model accuracy.

Contribution

The paper proposes SAA, a novel approach that identifies naive samples and applies more diverse augmentation, improving semi-supervised learning performance.

Findings

SAA improves FixMatch accuracy from 92.50% to 94.76%.

SAA enhances FlexMatch accuracy from 95.01% to 95.31%.

SAA is simple, lightweight, and effective.

Abstract

In semi-supervised learning, unlabeled samples can be utilized through augmentation and consistency regularization. However, we observed certain samples, even undergoing strong augmentation, are still correctly classified with high confidence, resulting in a loss close to zero. It indicates that these samples have been already learned well and do not provide any additional optimization benefits to the model. We refer to these samples as ``naive samples". Unfortunately, existing SSL models overlook the characteristics of naive samples, and they just apply the same learning strategy to all samples. To further optimize the SSL model, we emphasize the importance of giving attention to naive samples and augmenting them in a more diverse manner. Sample adaptive augmentation (SAA) is proposed for this stated purpose and consists of two modules: 1) sample selection module; 2) sample augmentation module. Specifically, the sample selection module picks out {naive samples} based on historical training information at each epoch, then the naive samples will be augmented in a more diverse manner in the sample augmentation module. Thanks to the extreme ease of implementation of the above modules, SAA is advantageous for being simple and lightweight. We add SAA on top of FixMatch and FlexMatch respectively, and experiments demonstrate SAA can significantly improve the models. For example, SAA helped improve the accuracy of FixMatch from 92.50% to 94.76% and that of FlexMatch from 95.01% to 95.31% on CIFAR-10 with 40 labels.