Co-training methods exploit predicted labels on the unlabeled data and select samples based on prediction confidence to augment the training.
the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space.