Enhancing Sample Utilization through Sample Adaptive Augmentation in
  Semi-Supervised Learning
Guan Gui Zhen Zhao Lei Qi Luping Zhou Lei Wang Yinghuan Shi
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.