activity
20202022
most citedEcoNAS: Finding Proxies for Economical Neural Architecture Search

10 citations · 20 across the 3 of their papers we have counts for

collaborators

5 papers

cs.CV2022

Federated Unsupervised Domain Adaptation for Face Recognition

Weiming Zhuang, Xin Gan, Yonggang Wen +3

Given labeled data in a source domain, unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, whose data distributions a…

cs.CV202110 cited

Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning

Weiming Zhuang, Xin Gan, Yonggang Wen +3

Unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, given labeled data in a source domain, whose data distributions d…

cs.CV2020

Performance Optimization for Federated Person Re-identification via Benchmark Analysis

Weiming Zhuang, Yonggang Wen, Xuesen Zhang +5

Federated learning is a privacy-preserving machine learning technique that learns a shared model across decentralized clients. It can alleviate privacy concerns of personal re-iden…

cs.CV202010 cited

EcoNAS: Finding Proxies for Economical Neural Architecture Search

Dongzhan Zhou, Xinchi Zhou, Wenwei Zhang +4

Neural Architecture Search (NAS) achieves significant progress in many computer vision tasks. While many methods have been proposed to improve the efficiency of NAS, the search pro…

cs.CV2020

MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identification

Yushi Lan, Yuan Liu, Maoqing Tian +4

Although person re-identification (ReID) has achieved significant improvement recently by enforcing part alignment, it is still a challenging task when it comes to distinguishing v…