DMT: Dynamic mutual training for semi-supervised learning

Recent semi-supervised learning methods use pseudo supervision as core idea, especially self-training methods that generate pseudo labels. However, pseudo labels are unreliable. Self-training methods usually rely on single model prediction confidence to filter low-confidence pseudo labels, thus remaining high-confidence errors and wasting many low-confidence correct labels. In this paper, we point out it is difficult for a model to counter its own errors. Instead, leveraging inter-model disagreement between different models is a key to locate pseudo label errors. With this new viewpoint, we propose mutual training between two different models by a dynamically re-weighted loss function, called Dynamic Mutual Training (DMT). We quantify inter-model disagreement by comparing predictions from two different models to dynamically re-weight loss in training, where a larger disagreement indicates a possible error and corresponds to a lower loss value. Extensive experiments show that DMT achieves state-of-the-art performance in both image classification and semantic segmentation. Our codes are released at https:///github.com/voldemortX/DST-CBC. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

Recent semi-supervised learning methods have used pseudo supervision, particularly self-training methods, which generate unreliable pseudo labels. This paper proposes a new approach called Dynamic Mutual Training (DMT) that leverages inter-model disagreement to locate pseudo label errors and achieve state-of-the-art performance in image classification and semantic segmentation.