Debiased Pseudo Labeling in Self-Training

Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. However, large-scale annotations are time-consuming and labor-exhaustive to obtain on realistic tasks. To mitigate the requirement for labeled data, self-training is widely used in both academia and industry by pseudo labeling on readily-available unlabeled data. Despite its popularity, pseudo labeling is well-believed to be unreliable and often leads to training instability. Our experimental studies further reveal that the performance of self-training is biased due to data sampling, pre-trained models, and training strategies, especially the inappropriate utilization of pseudo labels. To this end, we propose Debiased, in which the generation and utilization of pseudo labels are decoupled by two independent heads. To further improve the quality of pseudo labels, we introduce a worst-case estimation of pseudo labeling and seamlessly optimize the representations to avoid the worst-case. Extensive experiments justify that the proposed Debiased not only yields an average improvement of $14.4$\% against state-of-the-art algorithms on $11$ tasks (covering generic object recognition, fine-grained object recognition, texture classification, and scene classification) but also helps stabilize training and balance performance across classes.