  Deep learning for supervised learning has achieved astonishing performance in
various machine learning applications. However, annotated data is expensive and
rare. In practice, only a small portion of data samples are annotated.
Pseudo-ensembling-based approaches have achieved state-of-the-art results in
computer vision related tasks. However, it still relies on the quality of an
initial model built by labeled data. Less labeled data may degrade model
performance a lot. Domain constraint is another way regularize the posterior
but has some limitation. In this paper, we proposed a fuzzy
domain-constraint-based framework which loses the requirement of traditional
constraint learning and enhances the model quality for semi supervision.
Simulations results show the effectiveness of our design.
