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Ferrante Neri

2 accepted papers

2023

Homeomorphism Alignment for Unsupervised Domain Adaptation

Lihua Zhou, Mao Ye, Xiatian Zhu, Siying Xiao, Xu-Qian Fan, Ferrante Neri
ICCV 2023poster

Existing unsupervised domain adaptation (UDA) methods rely on aligning the features from the source and target domains explicitly or implicitly in a common space (i.e., the domain invariant space). Explicit distribution matching ignores the discriminability of learned features, while the implicit co…

Cited by 14PDF ↗code ↗Scholar ↗
2023

Independent Feature Decomposition and Instance Alignment for Unsupervised Domain Adaptation

Qichen He, Siying Xiao, Mao Ye, Xiatian Zhu, Ferrante Neri, Dongde Hou
IJCAI 2023poster

Existing Unsupervised Domain Adaptation (UDA) methods typically attempt to perform knowledge transfer in a domain-invariant space explicitly or implicitly. In practice, however, the obtained features is often mixed with domain-specific information which causes performance degradation. To overcome th…

Cited by 6Source ↗PDF ↗code ↗Scholar ↗