Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning

While remarkable progress has been made in imbalanced supervised learning, less attention has been given to the setting of imbalanced semi-supervised learning (SSL) where not only are few labeled data provided, but the underlying data distribution can be severely imbalanced. Recent work requires both complicated sampling strategies of pseudo-labeled unlabeled data and distribution alignment of the pseudo-label distribution to accommodate this imbalance. We present a novel approach that relies only on a form of a distribution alignment but no sampling strategy where rather than aligning the pseudo-labels during inference, we move the distribution alignment component into the respective cross entropy loss computations for both the supervised and unsupervised losses. This alignment compensates for both imbalance in the data and the eventual distributional shift present during evaluation. Altogether, this provides a unified strategy that offers both significantly reduced training requirements and improved performance across both low and richly labeled regimes and over varying degrees of imbalance. In experiments, we validate the efficacy of our method on SSL variants of CIFAR10LT, CIFAR100-LT, and ImageNet-127. On ImageNet-127, our method shows 1.6% accuracy improvement over CReST with an 80% training time reduction and is competitive with other SOTA methods. Code is available at https: //github.com/google-research/crest