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.
@article{arxiv.1902.04177,
title = {Domain Constraint Approximation based Semi Supervision},
author = {Yifu Wu and Jin Wei and Rigoberto Roche},
journal= {arXiv preprint arXiv:1902.04177},
year = {2019}
}