Search Results for author:
Found 2 papers, 0 papers with code
NormAUG: Normalization-guided Augmentation for Domain Generalization
Our method includes two paths: the main path and the auxiliary (augmented) path.
MultiMatch: Multi-task Learning for Semi-supervised Domain Generalization
To address the task, we first analyze the theory of the multi-domain learning, which highlights that 1) mitigating the impact of domain gap and 2) exploiting all samples to train the model can effectively reduce the generalization error in each source domain so as to improve the quality of pseudo-labels.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
