Semi-Supervised Single- and Multi-Domain Regression with Multi-Domain Training
This work addresses regression problems in scenarios with limited labeled data across domains, which is incremental as it builds on existing multi-modal learning approaches.
The paper tackles multi-domain and single-domain regression using distinct unpaired labeled sets per domain and a large unlabeled set, proposing a Bayesian estimation with worst-case design. It demonstrates results in facial expression removal and audio-visual word recognition, comparing to recent multi-modal algorithms.
We address the problems of multi-domain and single-domain regression based on distinct and unpaired labeled training sets for each of the domains and a large unlabeled training set from all domains. We formulate these problems as a Bayesian estimation with partial knowledge of statistical relations. We propose a worst-case design strategy and study the resulting estimators. Our analysis explicitly accounts for the cardinality of the labeled sets and includes the special cases in which one of the labeled sets is very large or, in the other extreme, completely missing. We demonstrate our estimators in the context of removing expressions from facial images and in the context of audio-visual word recognition, and provide comparisons to several recently proposed multi-modal learning algorithms.