ProMatch: Semi-Supervised Learning with Prototype Consistency

Recent state-of-the-art semi-supervised learning (SSL) methods have made significant advancements by combining consistency-regularization and pseudo-labeling in a joint learning paradigm. The core concept of these methods is to identify consistency targets (pseudo-labels) by selecting predicted distributions with high confidence from weakly augmented unlabeled samples. However, they often face the problem of erroneous high confident pseudo-labels, which can lead to noisy training. This issue arises due to two main reasons: (1) when the model is poorly calibrated, the prediction of a single sample may be overconfident and incorrect, and (2) propagating pseudo-labels from unlabeled samples can result in error accumulation due to the margin between the pseudo-label and the ground-truth label. To address this problem, we propose a novel consistency criterion called Prototype Consistency (PC) to improve the reliability of pseudo-labeling by leveraging the prototype similarities between labeled and unlabeled samples. First, we instantiate semantic-prototypes (centers of embeddings) and prediction-prototypes (centers of predictions) for each category using memory buffers that store the features of labeled examples. Second, for a given unlabeled sample, we determine the most similar semantic-prototype and prediction-prototype by assessing the similarities between the features of the unlabeled sample and the prototypes of the labeled samples. Finally, instead of using the prediction of the unlabeled sample as the pseudo-label, we select the most similar prediction-prototype as the consistency target, as long as the predicted category of the most similar prediction-prototype, the ground-truth category of the most similar semantic-prototype, and the ground-truth category of the most similar prediction-prototype are equivalent. By combining the PC approach with the techniques developed by the MixMatch family, our proposed ProMatch framework demonstrates significant performance improvements compared to previous algorithms on datasets such as CIFAR-10, CIFAR-100, SVHN, and Mini-ImageNet.