DSDRNet: Disentangling Representation and Reconstruct Network for Domain Generalization

arXiv:2404.13848v1 Announce Type: new
Abstract: 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 …

abstract adversarial alignment arxiv augmentation challenges cs.cv data distribution domain domains ensemble labels meta meta-learning network paper representation shift solutions testing training type