Domain generalization faces challenges due to the distribution shift between training and testing sets, and the presence of unseen target domains. Common solutions include domain alignment, meta-learning, data augmentation, or ensemble learning, all of which rely on domain labels or domain adversarial techniques. In this paper, we propose a Dual-Stream Separation and Reconstruction Network, dubbed DSDRNet. It is a disentanglement-reconstruction approach that integrates features of both inter-instance and intra-instance through dual-stream fusion. The method introduces novel supervised signals by combining inter-instance semantic distance and intra-instance similarity. Incorporating Adaptive Instance Normalization (AdaIN) into a two-stage cyclic reconstruction process enhances self-disentangled reconstruction signals to facilitate model convergence. Extensive experiments on four benchmark datasets demonstrate that DSDRNet outperforms other popular methods in terms of domain generalization capabilities.

我们提出了一种名为DSDRNet的双流分离与重建网络，它是一种通过双流融合将inter-instance和intra-instance的特征相结合的解缠结重建方法。该方法通过结合inter-instance语义距离和intra-instance相似性引入了新颖的监督信号。将自适应实例归一化（AdaIN）引入到两阶段循环重建过程中，增强了自我解缠结重建信号以促进模型收敛。对四个基准数据集进行的大量实验证明，DSDRNet在域泛化能力方面优于其他热门方法。

DSDRNet: 基于解缠表示和重构网络的领域泛化