Discriminator-free adversarial domain adaptation with information balance

Hui Jiang; Di Wu; Xing Wei; Wenhao Jiang; Xiongbo Qing; Hui Jiang; Di Wu; Xing Wei; Wenhao Jiang; Xiongbo Qing

doi:10.3934/era.2025011

Electronic Research Archive

2025, Volume 33, Issue 1: 210-230. doi: 10.3934/era.2025011

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Research article

Discriminator-free adversarial domain adaptation with information balance

1.
Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei 230601, China
2.
School of Computer and Artificial Intelligence, Hefei Normal University, Hefei 230601, China
3.
School of Computer and Information, Hefei University of Technology, Hefei 230601, China

Received: 16 October 2024 Revised: 24 December 2024 Accepted: 03 January 2025 Published: 20 January 2025

In the realm of Unsupervised Domain Adaptation (UDA), adversarial learning has achieved significant progress. Existing adversarial UDA methods typically employ additional discriminators and feature extractors to engage in a max-min game. However, these methods often fail to effectively utilize the predicted discriminative information, thus resulting in the mode collapse of the generator. In this paper, we propose a Dynamic Balance-based Domain Adaptation (DBDA) method for self-correlated domain adaptive image classification. Instead of adding extra discriminators, we repurpose the classifier as a discriminator and introduce a dynamic balancing learning approach. This approach ensures an explicit domain alignment and category distinction, thus enabling DBDA to fully leverage the predicted discriminative information for an effective feature alignment. We conducted experiments on multiple datasets, therefore demonstrating that the proposed method maintains a robust classification performance across various scenarios.
- transfer learning,
- domain adaptation,
- image classification,
- discriminator-free adversarial learning,
- dynamic balancing
Citation: Hui Jiang, Di Wu, Xing Wei, Wenhao Jiang, Xiongbo Qing. Discriminator-free adversarial domain adaptation with information balance[J]. Electronic Research Archive, 2025, 33(1): 210-230. doi: 10.3934/era.2025011

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Abstract

In the realm of Unsupervised Domain Adaptation (UDA), adversarial learning has achieved significant progress. Existing adversarial UDA methods typically employ additional discriminators and feature extractors to engage in a max-min game. However, these methods often fail to effectively utilize the predicted discriminative information, thus resulting in the mode collapse of the generator. In this paper, we propose a Dynamic Balance-based Domain Adaptation (DBDA) method for self-correlated domain adaptive image classification. Instead of adding extra discriminators, we repurpose the classifier as a discriminator and introduce a dynamic balancing learning approach. This approach ensures an explicit domain alignment and category distinction, thus enabling DBDA to fully leverage the predicted discriminative information for an effective feature alignment. We conducted experiments on multiple datasets, therefore demonstrating that the proposed method maintains a robust classification performance across various scenarios.

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