KL Guided Domain Adaptation

TL;DR

This paper introduces a theoretically grounded, efficient domain adaptation method that minimizes reverse KL divergence between source and target representations, improving generalization without complex adversarial training.

Contribution

It derives a new generalization bound based on reverse KL divergence and proposes a practical, stable algorithm that outperforms existing marginal alignment techniques.

Findings

Outperforms other representation-alignment methods in experiments

Efficient estimation of KL divergence via minibatch samples

No need for additional networks or adversarial objectives

Abstract

Domain adaptation is an important problem and often needed for real-world applications. In this problem, instead of i.i.d. training and testing datapoints, we assume that the source (training) data and the target (testing) data have different distributions. With that setting, the empirical risk minimization training procedure often does not perform well, since it does not account for the change in the distribution. A common approach in the domain adaptation literature is to learn a representation of the input that has the same (marginal) distribution over the source and the target domain. However, these approaches often require additional networks and/or optimizing an adversarial (minimax) objective, which can be very expensive or unstable in practice. To improve upon these marginal alignment techniques, in this paper, we first derive a generalization bound for the target loss based on the training loss and the reverse Kullback-Leibler (KL) divergence between the source and the target representation distributions. Based on this bound, we derive an algorithm that minimizes the KL term to obtain a better generalization to the target domain. We show that with a probabilistic representation network, the KL term can be estimated efficiently via minibatch samples without any additional network or a minimax objective. This leads to a theoretically sound alignment method which is also very efficient and stable in practice. Experimental results also suggest that our method outperforms other representation-alignment approaches.