Haohan Wang

2.1k citations
32 papers · 637 indexed · 1 hit paper · h-index 9
Topics
Multimodal Machine Learning Applications (7 papers)Domain Adaptation and Few-Shot Learning (7 papers)Adversarial Robustness in Machine Learning (3 papers)

In The Last Decade

Haohan Wang

29 papers receiving 627 citations

Hit Papers

High-Frequency Component Helps Explain the Generalization...20202026202220242020100200300

Peers

Haohan Wang
Comparison fields: 5 of 99
  • Artificial Intelligence 321
  • Computer Vision and Pattern Recognition 304
  • Media Technology 63
  • Industrial and Manufacturing Engineering 46
  • Signal Processing 40
Replace Zhisheng Zhong with:
Zhisheng Zhong China
Zhuang Liu China
Samuel Dodge United States
Zhijiang Zhang China
Min Meng China
Shu Liu China
Francisco Massa
Mahmut Kaya Türkiye
Yingtian Zou Singapore
Xianxu Hou China
Haohan Wang relative to Zhisheng Zhong China Zhisheng Zhong's profile →
Citations per field
00.5×1.5×2.5×
Zhisheng Zhong · 1×
Citations per year

Countries citing papers authored by Haohan Wang

Since Specialization
Citations

This map shows the geographic impact of Haohan Wang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Haohan Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Haohan Wang more than expected).

Fields of papers citing papers by Haohan Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Haohan Wang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Haohan Wang. The network helps show where Haohan Wang may publish in the future.

Co-authorship network of co-authors of Haohan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Haohan Wang. A scholar is included among the top collaborators of Haohan Wang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Haohan Wang. Haohan Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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15 4
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Learning Robust Global Representations by Penalizing Local Predictive Power
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19 86
20 8

About Haohan Wang

Haohan Wang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Ceramics and Composites, having authored 32 papers that have together received 637 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (7 papers), Domain Adaptation and Few-Shot Learning (7 papers) and Adversarial Robustness in Machine Learning (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (304 citations), Artificial Intelligence (321 citations) and Media Technology (63 citations). Haohan Wang has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Eric P. Xing, Zeyi Huang, Xindi Wu, Sheng Zha, He He, Haoqian Wang, Zhuoling Li, Zachary C. Lipton, Zexue He and Songwei Ge. Their work appears in journals such as Chemosphere, Corrosion Science and Composites Part B Engineering.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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