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researcher

Jun Wang

16 papers hereh-index 7121 citations28 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author11
  • last author4

Across the 15 of 16 papers where every author was matched, so the position is known.

fields
  • cs.IR7
  • cs.CV4
  • cs.CL2
  • cs.AI1
  • cs.LG1
  • cs.SE1
same name
  • Jun Wang — 64 papers, h 35
  • Jun Wang — 55 papers, h 47
  • Jun Wang — 27 papers, h 12
  • Jun Wang — 20 papers, h 4
  • Jun Wang — 18 papers, h 11
  • Jun Wang — 17 papers, h 11

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20232026
most citedConsistent Assistant Domains Transformer for Source-free Domain Adaptation

3 citations · 6 across the 16 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2025★ 1 cited

Attention Residual Fusion Network with Contrast for Source-free Domain Adaptation

Renrong Shao, Wei Zhang, Jun Wang

Source-free domain adaptation (SFDA) involves training a model on source domain and then applying it to a related target domain without access to the source data and labels during…

cs.CV2025★ 3 cited

Consistent Assistant Domains Transformer for Source-free Domain Adaptation

Renrong Shao, Wei Zhang, Kangyang Luo +2

Source-free domain adaptation (SFDA) aims to address the challenge of adapting to a target domain without accessing the source domain directly. However, due to the inaccessibility…

cs.CV2024

Data-free Knowledge Distillation for Fine-grained Visual Categorization

Renrong Shao, Wei Zhang, Jianhua Yin +1

Data-free knowledge distillation (DFKD) is a promising approach for addressing issues related to model compression, security privacy, and transmission restrictions. Although the ex…

cs.CV2023

DMT: Comprehensive Distillation with Multiple Self-supervised Teachers

Yuang Liu, Jing Wang, Qiang Zhou +3

Numerous self-supervised learning paradigms, such as contrastive learning and masked image modeling, have been proposed to acquire powerful and general representations from unlabel…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.