  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.
