Self-Training Domain Adaptation Via Weight Transmission Between Generators

Abstract

Unsupervised domain adaptation (UDA) aims to transfer knowledge from the labeled source domain to the fully-unlabeled target domain, thus improving the classification performance of the target domain. Recently, self-training has shown its effectiveness on UDA. However, the feature space for generating pseudo-labels contains a large amount of source information, making it challenging for the generator to learn discriminative features of the target domain. In this paper, we propose a self-training domain adaptation model via weight transmission between generators (WTBG). Specifically, we develop a bi-directional transmission structure for generators, using Exponential Moving Average (EMA) as the bridge between two generators. By cyclically transmitting weight parameters between them, alleviate the difficulty of generators in learning target features. And a pseudo-label filter based on cosine similarity is designed to reduce the influence of error pseudo-labels. Extensive experiments conducted on two benchmark UDA datasets show that WTBG has superior classification performance.