Diverse Target and Contribution Scheduling for Domain Generalization

• URL: http://arxiv.org/abs/2309.16460v1
• Date: Thu, 28 Sep 2023 14:10:25 GMT
• Title: Diverse Target and Contribution Scheduling for Domain Generalization
• Authors: Shaocong Long, Qianyu Zhou, Chenhao Ying, Lizhuang Ma, Yuan Luo
• Abstract summary: Generalization under the distribution shift has been a great challenge in computer vision. We propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS) DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB)
• Score: 34.29652655561764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization~(DG) can lead to gradient conflicts, making it insufficient for capturing the intrinsic class characteristics and hard to increase the intra-class variation. Besides, existing methods in DG mostly overlook the distinct contributions of source (seen) domains, resulting in uneven learning from these domains. To address these issues, we firstly present a theoretical and empirical analysis of the existence of gradient conflicts in DG, unveiling the previously unexplored relationship between distribution shifts and gradient conflicts during the optimization process. In this paper, we present a novel perspective of DG from the empirical source domain's risk and propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB), with the aim of addressing the limitations associated with the common utilization of one-hot labels and equal contributions for source domains in DG. In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains. Extensive experiments with analysis on four benchmark datasets show that the proposed method achieves a competitive performance in comparison with the state-of-the-art approaches, demonstrating the effectiveness and advantages of the proposed DTCS.

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