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pro vyhledávání: '"Cui, Shuhao"'
Autor:
Duan, Xiaoyue, Cui, Shuhao, Kang, Guoliang, Zhang, Baochang, Fei, Zhengcong, Fan, Mingyuan, Huang, Junshi
Consistent editing of real images is a challenging task, as it requires performing non-rigid edits (e.g., changing postures) to the main objects in the input image without changing their identity or attributes. To guarantee consistent attributes, som
Externí odkaz:
http://arxiv.org/abs/2312.14611
Due to the domain discrepancy in visual domain adaptation, the performance of source model degrades when bumping into the high data density near decision boundary in target domain. A common solution is to minimize the Shannon Entropy to push the deci
Externí odkaz:
http://arxiv.org/abs/2107.06154
Semi-supervised domain adaptation (SSDA) aims to solve tasks in target domain by utilizing transferable information learned from the available source domain and a few labeled target data. However, source data is not always accessible in practical sce
Externí odkaz:
http://arxiv.org/abs/2107.03008
Publikováno v:
NeurIPS 2020
In visual domain adaptation (DA), separating the domain-specific characteristics from the domain-invariant representations is an ill-posed problem. Existing methods apply different kinds of priors or directly minimize the domain discrepancy to addres
Externí odkaz:
http://arxiv.org/abs/2011.14540
In unsupervised domain adaptation, rich domain-specific characteristics bring great challenge to learn domain-invariant representations. However, domain discrepancy is considered to be directly minimized in existing solutions, which is difficult to a
Externí odkaz:
http://arxiv.org/abs/2003.13183
The learning of the deep networks largely relies on the data with human-annotated labels. In some label insufficient situations, the performance degrades on the decision boundary with high data density. A common solution is to directly minimize the S
Externí odkaz:
http://arxiv.org/abs/2003.12237
We address the unsupervised open domain recognition (UODR) problem, where categories in labeled source domain S is only a subset of those in unlabeled target domain T. The task is to correctly classify all samples in T including known and unknown cat
Externí odkaz:
http://arxiv.org/abs/1904.08631
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