Existing semi-supervised learning (SSL) algorithms typically assume classbalanced datasets, although the class distributions of many real-world datasets are imbalanced. In general, classifiers trained on a class-imbalanced dataset are biased toward the majority classes. This issue becomes more problematic for SSL algorithms because they utilize the biased prediction of unlabeled data for training. However, traditional class-imbalanced learning techniques, which are designed for labeled data, cannot be readily combined with SSL algorithms. We propose a scalable class-imbalanced SSL algorithm that can effectively use unlabeled data, while mitigating class imbalance by introducing an auxiliary balanced classifier (ABC) of a single layer, which is attached to a representation layer of an existing SSL algorithm. The ABC is trained with a class-balanced loss of a minibatch, while using high-quality representations learned from all data points in the minibatch using the backbone SSL algorithm to avoid overfitting and information loss. Moreover, we use consistency regularization, a recent SSL technique for utilizing unlabeled data in a modified way, to train the ABC to be balanced among the classes by selecting unlabeled data with the same probability for each class. The proposed algorithm achieves state-of-the-art performance in various class-imbalanced SSL experiments using four benchmark datasets.

Following from the discussion in Section 3.1, we want to maximize E [zy (x+)]. B.1 Higher Noise Level In the main paper, we conduct experiments on CIFAR-10 using noise level =0 .25 only. Here, we report our main set of results on CIFAR-10 (Table 3) using higher values. In Table 8, we report results using =0 .5 and in Table 9, we report results using =1 .0. B.2 Using ViT [6] In the main paper, we used Convolutional Neural Network (CNN) based architectures.

However, the majority of work on self-training has focused on the objective of improving accuracy whereas practical machine learning systems can havecomplex goals (e.g.

Generative Adversarial Networks (GANs) are powerful tools for image synthesis.