In this paper we introduce SemiGPC, a distribution-aware label refinement
strategy based on Gaussian Processes where the predictions of the model are
derived from the labels posterior distribution. Differently from other
buffer-based semi-supervised methods such as CoMatch and SimMatch, our SemiGPC
includes a normalization term that addresses imbalances in the global data
distribution while maintaining local sensitivity. This explicit control allows
SemiGPC to be more robust to confirmation bias especially under class
imbalance. We show that SemiGPC improves performance when paired with different
Semi-Supervised methods such as FixMatch, ReMixMatch, SimMatch and FreeMatch
and different pre-training strategies including MSN and Dino. We also show that
SemiGPC achieves state of the art results under different degrees of class
imbalance on standard CIFAR10-LT/CIFAR100-LT especially in the low data-regime.
Using SemiGPC also results in about 2% avg.accuracy increase compared to a new
competitive baseline on the more challenging benchmarks SemiAves, SemiCUB,
SemiFungi and Semi-iNat.

本文中介绍了 SemiGPC，一种基于高斯过程的分布感知标签细化策略，其中模型的预测源自于标签的后验分布。SemiGPC 通过包含归一化项来解决全局数据分布不平衡同时保持局部敏感度，从而更好地应对确认偏差特别是在类别不平衡条件下。研究结果表明，SemiGPC 在与 FixMatch、ReMixMatch、SimMatch、FreeMatch 等不同的半监督方法和 MSN、Dino 等不同的预训练策略配对时，可以提高性能；在标准的 CIFAR10-LT/CIFAR100-LT 的不同类别不平衡程度下，SemiGPC 取得了最先进的结果，在更具挑战性的基准测试 SemiAves、SemiCUB、SemiFungi 和 Semi-iNat 上，与新的竞争性基线相比，使用 SemiGPC 还能提高约 2% 的平均准确率。