  Self-training is a powerful approach to deep learning. The key process is to
find a pseudo-label for modeling. However, previous self-training algorithms
suffer from the over-confidence issue brought by the hard labels, even some
confidence-related regularizers cannot comprehensively catch the uncertainty.
Therefore, we propose a new self-training framework to combine uncertainty
information of both model and dataset. Specifically, we propose to use
Expectation-Maximization (EM) to smooth the labels and comprehensively estimate
the uncertainty information. We further design a basis extraction network to
estimate the initial basis from the dataset. The obtained basis with
uncertainty can be filtered based on uncertainty information. It can then be
transformed into the real hard label to iteratively update the model and basis
in the retraining process. Experiments on image classification and semantic
segmentation show the advantages of our methods among confidence-aware
self-training algorithms with 1-3 percentage improvement on different datasets.
