Web: http://arxiv.org/abs/2201.11963

Jan. 31, 2022, 2:11 a.m. | Changwei Xu, Jianfei Yang, Haoran Tang, Han Zou, Cheng Lu, Tianshuo Zhang

cs.LG updates on arXiv.org arxiv.org

Unsupervised Domain Adaptation (UDA), a branch of transfer learning where
labels for target samples are unavailable, has been widely researched and
developed in recent years with the help of adversarially trained models.
Although existing UDA algorithms are able to guide neural networks to extract
transferable and discriminative features, classifiers are merely trained under
the supervision of labeled source data. Given the inevitable discrepancy
between source and target domains, the classifiers can hardly be aware of the
target classification boundaries. In …

arxiv augmentation cv data domain adaptation features unsupervised

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