DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization
<|reference_start|>DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization: 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.<|reference_end|>
@article{yang2024dsdrnet:,
    title={DSDRNet: Disentangling Representation and Reconstruct Network for Domain
  Generalization},
    author={Juncheng Yang, Zuchao Li, Shuai Xie, Wei Yu, Shijun Li},
    journal={arXiv preprint arXiv:2404.13848},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.13848},
    primaryClass={cs.CV}
}