A Mutually Attentive Co-Training Framework for Semi-Supervised Recognition

Abstract

Self-training plays an important role in practical recognition applications where sufficient clean labels are unavailable. Existing methods focus on generating reliable pseudo labels to retrain a model, while ignoring the importance of improving model reliability to those inevitably mislabeled data. In this paper, we propose a novel Mutually Attentive Co-training Framework (MACF) that can effectively alleviate the negative impacts of incorrect labels on model retraining by exploring deep model disagreements. Specifically, MACF trains two symmetrical sub-networks that have the same input and are connected by several attention modules at different layers. Each attention module analyzes the inferred features from two sub-networks for the same input and feedback attention maps for them to indicate noisy gradients. This is realized by exploring the back-propagation process of incorrect labels at different layers to design attention modules. By multi-layer interception, the noisy gradients caused by incorrect labels can be effectively reduced for both sub-networks, leading to robust training to potential incorrect labels. In addition, a hierarchical distillation strategy is developed to improve the pseudo labels by aggregating the predictions from multi-models and data transformations. The experiments on six general benchmarks, including classification and biomedical segmentation, demonstrate that MACF is much robust to noisy labels than previous methods.