Yunchen Pu

2.6k citations
30 papers · 529 · h-index 11

Impact in

Papers in

    • Generative Adversarial Networks and Image Synthesis 9
    • Advanced Image and Video Retrieval Techniques 4
    • Domain Adaptation and Few-Shot Learning 6
    • Natural Language Processing Techniques 4
    • AI in cancer detection 3
    • Adversarial Robustness in Machine Learning 3

Yunchen Pu

29 papers receiving 497 citations

Peers

Yunchen Pu
Comparison fields: 5 of 75
  • Acoustics and Ultrasonics 21
  • Computer Vision and Pattern Recognition 307
  • Media Technology 67
  • Artificial Intelligence 229
  • Computational Mechanics 120
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Citations per year

Countries citing papers authored by Yunchen Pu

Since Specialization
Citations

This map shows the geographic impact of Yunchen Pu'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 Yunchen Pu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yunchen Pu more than expected).

Fields of papers citing papers by Yunchen Pu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yunchen Pu. 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 Yunchen Pu. The network helps show where Yunchen Pu may publish in the future.

Co-authors

The 25 scholars most cited alongside Yunchen Pu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Yunchen Pu Line = papers co-authored together Yunchen Pu links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 30 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019145
2 201874
3
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching
201757
4 201854
5 201740
6
Triangle Generative Adversarial Networks
201730
7 201624
8 201819
9 201718
10
Symmetric Variational Autoencoder and Connections to Adversarial Learning
201812
11
Adversarial Symmetric Variational Autoencoder
201711
12 20166
13 20165
14 20175
15
A generative model for deep convolutional learning
20154
16 20174
17
Unsupervised Learning of Sentence Representations using Convolutional Neural Networks
20163
18
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
20183
19 20193
20 20162

About Yunchen Pu

Yunchen Pu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Biomedical Engineering and Signal Processing, having authored 30 papers that have together received 529 indexed citations. Recurring topics across this work include Generative Adversarial Networks and Image Synthesis (9 papers), Domain Adaptation and Few-Shot Learning (6 papers), Natural Language Processing Techniques (4 papers), Advanced Image and Video Retrieval Techniques (4 papers), Sparse and Compressive Sensing Techniques (4 papers), AI in cancer detection (3 papers), Random lasers and scattering media (3 papers) and Adversarial Robustness in Machine Learning (3 papers). The work is most often cited by research in Acoustics and Ultrasonics (21 citations), Computer Vision and Pattern Recognition (307 citations), Media Technology (67 citations), Artificial Intelligence (229 citations) and Computational Mechanics (120 citations). Yunchen Pu has collaborated with scholars based in United States, United Kingdom and China. Frequent co-authors include Xin Yuan, Lawrence Carin, Vassilis Athitsos, Xin Miao, Zhe Gan, Chunyuan Li, Changyou Chen, Ricardo Henao, Yizhe Zhang and Vinay Kumar Verma. Their work appears in journals such as Microscopy and Microanalysis, Optics Express, International Journal of Human-Computer Interaction, The Journal of the Acoustical Society of America and Journal of Machine Learning Research.

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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