  We propose a novel semi-supervised learning (SSL) method that adopts
selective training with pseudo labels. In our method, we generate hard
pseudo-labels and also estimate their confidence, which represents how likely
each pseudo-label is to be correct. Then, we explicitly select which
pseudo-labeled data should be used to update the model. Specifically, assuming
that loss on incorrectly pseudo-labeled data sensitively increase against data
augmentation, we select the data corresponding to relatively small loss after
applying data augmentation. The confidence is used not only for screening
candidates of pseudo-labeled data to be selected but also for automatically
deciding how many pseudo-labeled data should be selected within a mini-batch.
Since accurate estimation of the confidence is crucial in our method, we also
propose a new data augmentation method, called MixConf, that enables us to
obtain confidence-calibrated models even when the number of training data is
small. Experimental results with several benchmark datasets validate the
advantage of our SSL method as well as MixConf.
