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.

本文通过理论和实证分析揭示了领域通用中梯度冲突的存在，并提出了一种基于经验源领域风险的全新视角及相应的解决方案（DTCS），通过多样化目标监督和多样化贡献平衡两个创新模块，来解决常用的单热标签和源领域平等贡献的局限性，实验证明该方法在四个基准数据集上具有较高的竞争性表现，从而展示了 DTCS 方法的有效性和优势。