f-Domain-Adversarial Learning: Theory and Algorithms

Understand

Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labeled dataset.

• In this paper, we introduce a novel and general domain-adversarial framework.
• Specifically, we derive a novel generalization bound for domain adaptation that exploits a new measure of discrepancy between distributions based on a variational characterization of f-divergences.
• It recovers the theoretical results from Ben-David et al.