Teaching Others Teaches Yourself: Semi-supervised Ensembled Pseudo-labeling Method for Image Classification

Abstract

Semi-supervised methods have recently received significant attention in deep learning because they are able to reduce the dependence on labeled data while ensuring good performance. The pseudo-label based method has been widely used as a classic semi-supervised method but still suffers from the confirmation bias problem, which causes significant damage to the model’s training process. To solve this problem, we propose an ensembled semi-supervised framework, which can effectively improve the quality of pseudo-labels by ensembling the prediction results of multiple models to generate pseudo-labels. Simultaneously, we innovatively design a Ensembled Divergence Promotion (EDP) method to increase the diversity among different models for better ensembling results. Extensive experiments are conducted on CIFAR-10 and CIFAR-100, showing superior performance of the proposed method to the state-of-the-art (SOTA) semi-supervised method. Meanwhile, extensive ablation experiments and visualization results are conducted to prove the effectiveness of our method.