Asymmetric Tri-training for Unsupervised Domain Adaptation

Understand

Deep-layered models trained on a large number of labeled samples boost the accuracy of many tasks.

• It is important to apply such models to different domains because collecting many labeled samples in various domains is expensive.
• In unsupervised domain adaptation, one needs to train a classifier that works well on a target domain when provided with labeled source samples and unlabeled target samples.
• Although many methods aim to match the distributions of source and target samples, simply matching the distribution cannot ensure accuracy on the target domain.