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cs.LG2023
Mixture Data for Training Cannot Ensure Out-of-distribution Generalization
Songming Zhang, Yuxiao Luo, Qizhou Wang +4
Deep neural networks often face generalization problems to handle out-of-distribution (OOD) data, and there remains a notable theoretical gap between the contributing factors and t…
cs.LG2023
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
Ruijiang Dong, Feng Liu, Haoang Chi +5
Generating unlabeled data has been recently shown to help address the few-shot hypothesis adaptation (FHA) problem, where we aim to train a classifier for the target domain with a…