f-Domain-Adversarial Learning: Theory and Algorithms

by   David Acuna, et al.

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. (2010a) as a special case and supports divergences used in practice. Based on this bound, we derive a new algorithmic framework that introduces a key correction in the original adversarial training method of Ganin et al. (2016). We show that many regularizers and ad-hoc objectives introduced over the last years in this framework are then not required to achieve performance comparable to (if not better than) state-of-the-art domain-adversarial methods. Experimental analysis conducted on real-world natural language and computer vision datasets show that our framework outperforms existing baselines, and obtains the best results for f-divergences that were not considered previously in domain-adversarial learning.


page 1

page 2

page 3

page 4


Bridging Theory and Algorithm for Domain Adaptation

This paper addresses the problem of unsupervised domain adaption from th...

Domain Adversarial Neural Networks for Domain Generalization: When It Works and How to Improve

Theoretically, domain adaptation is a well-researched problem. Further, ...

Adversarial Learning and Explainability in Structured Datasets

We theoretically and empirically explore the explainability benefits of ...

Continual Unsupervised Domain Adaptation with Adversarial Learning

Unsupervised Domain Adaptation (UDA) is essential for autonomous driving...

FRuDA: Framework for Distributed Adversarial Domain Adaptation

Breakthroughs in unsupervised domain adaptation (uDA) can help in adapti...

Unsupervised Multi-Class Domain Adaptation: Theory, Algorithms, and Practice

In this paper, we study the formalism of unsupervised multi-class domain...

Unsupervised Domain Adaptation Meets Offline Recommender Learning

To construct a well-performing recommender offline, eliminating selectio...

Please sign up or login with your details

Forgot password? Click here to reset