Multi-Source and Multi-Target Domain Adaptation Based on Dynamic Generator with Attention
Yuwu Lu, Haoyu Huang, Biqing Zeng, Zhihui Lai, Xuelong Li
Abstract
As a branch of domain adaptation (DA), multi-source DA (MSDA) is a challenging issue that aims to transfer knowledge from multiple well-labeled source domains to a target domain for target tasks. However, most existing related works focus on single-target domain adaptation, and multiple target domain adaptation is not accounted for. We believe that multiple target domains provide valuable knowledge. Meanwhile, in multi-source and multi-target adaptation scenarios, feature generators with static parameters have difficulty generating deep features of each individual domain. In this paper, we propose a Dynamic Generator With Attention (DGWA) method for multi-source and multi-target domain adaptation to adapt domain-agnostic deep features in a multi-source and multi-target domain scenario. The feature generator with dynamic parameters can dynamically change its parameters with data input from different domains, which greatly improves the generalization of the feature pools. An attention mechanism is used in our DGWA to learn more transferable information from different domains. To demonstrate the performance of DGWA, we conduct extensive experiments on several popular domain adaptation datasets, including the digits, Office+Caltech10, Office-Home, and ImageCLEF-DA datasets. The experimental results demonstrate that our method performs better than state-of-the-art methods.