3 citations · 6 across the 5 of their papers we have counts for
5 papers
Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
Yuan Yao, Jin Song, Huixia Li +3
Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source domain. The source domain typically contains semantically meaningful sa…
Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective
Yuan Yao, Xiaopu Zhang, Yu Zhang +2
Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning across domains with distinct feature representations and distributions, where source samples are labeled w…
Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding Perspective
Yulong Zhang, Yuan Yao, Shuhao Chen +4
Empirical Risk Minimization (ERM) is fragile in scenarios with insufficient labeled samples. A vanilla extension of ERM to unlabeled samples is Entropy Minimization (EntMin), which…
Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network
Yuan Yao, Xutao Li, Yu Zhang +1
Heterogeneous domain adaptation (HDA) tackles the learning of cross-domain samples with both different probability distributions and feature representations. Most of the existing H…
Heterogeneous Domain Adaptation via Soft Transfer Network
Yuan Yao, Yu Zhang, Xutao Li +1
Heterogeneous domain adaptation (HDA) aims to facilitate the learning task in a target domain by borrowing knowledge from a heterogeneous source domain. In this paper, we propose a…