Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyond

TL;DR

This paper provides a new, improved theoretical analysis of gradual domain adaptation, reducing the error bound's dependency on the number of intermediate domains from exponential to linear, and suggests optimal strategies for constructing domain paths.

Contribution

It introduces a significantly tighter generalization bound for gradual self-training, relaxing previous assumptions and guiding the optimal design of intermediate domain paths.

Findings

The new bound depends linearly on the number of domains T.

An optimal number of intermediate domains T minimizes the generalization error.

Empirical results validate the theoretical improvements on real datasets.

Abstract

The vast majority of existing algorithms for unsupervised domain adaptation (UDA) focus on adapting from a labeled source domain to an unlabeled target domain directly in a one-off way. Gradual domain adaptation (GDA), on the other hand, assumes a path of $(T-1)$ unlabeled intermediate domains bridging the source and target, and aims to provide better generalization in the target domain by leveraging the intermediate ones. Under certain assumptions, Kumar et al. (2020) proposed a simple algorithm, Gradual Self-Training, along with a generalization bound in the order of $e^{O(T)} \left(\varepsilon_0+O\left(\sqrt{log(T)/n}\right)\right)$ for the target domain error, where $\varepsilon_0$ is the source domain error and $n$ is the data size of each domain. Due to the exponential factor, this upper bound becomes vacuous when $T$ is only moderately large. In this work, we analyze gradual self-training under more general and relaxed assumptions, and prove a significantly improved generalization bound as $\varepsilon_0+ O \left(T\Delta + T/\sqrt{n}\right) + \widetilde{O}\left(1/\sqrt{nT}\right)$, where $\Delta$ is the average distributional distance between consecutive domains. Compared with the existing bound with an exponential dependency on $T$ as a multiplicative factor, our bound only depends on $T$ linearly and additively. Perhaps more interestingly, our result implies the existence of an optimal choice of $T$ that minimizes the generalization error, and it also naturally suggests an optimal way to construct the path of intermediate domains so as to minimize the accumulative path length $T\Delta$ between the source and target. To corroborate the implications of our theory, we examine gradual self-training on multiple semi-synthetic and real datasets, which confirms our findings. We believe our insights provide a path forward toward the design of future GDA algorithms.