Pseudo-label-based semi-supervised learning (SSL) has achieved great success
on raw data utilization. However, its training procedure suffers from
confirmation bias due to the noise contained in self-generated artificial
labels. Moreover, the model's judgment becomes noisier in real-world
applications with extensive out-of-distribution data. To address this issue, we
propose a general method named Class-aware Contrastive Semi-Supervised Learning
(CCSSL), which is a drop-in helper to improve the pseudo-label quality and
enhance the model's robustness in the real-world setting. Rather than treating
real-world data as a union set, our method separately handles reliable
in-distribution data with class-wise clustering for blending into downstream
tasks and noisy out-of-distribution data with image-wise contrastive for better
generalization. Furthermore, by applying target re-weighting, we successfully
emphasize clean label learning and simultaneously reduce noisy label learning.
Despite its simplicity, our proposed CCSSL has significant performance
improvements over the state-of-the-art SSL methods on the standard datasets
CIFAR100 and STL10. On the real-world dataset Semi-iNat 2021, we improve
FixMatch by 9.80% and CoMatch by 3.18%. Code is available
this https URL

提出了一种名为 CCSSL 的半监督学习方法，采用类别聚类和图像对比改善模型的伪标签质量和在真实世界中的鲁棒性，并通过目标重新加权实现了干净标签学习和减少噪声标签学习。实验表明其在多个数据集上均取得了显著的性能提升。