Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
201712.5k citationsJun-Yan Zhu, Taesung Park et al.profile →
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
This map shows the geographic impact of Jun-Yan Zhu'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 Jun-Yan Zhu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun-Yan Zhu more than expected).
This network shows the impact of papers produced by Jun-Yan Zhu. 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 Jun-Yan Zhu. The network helps show where Jun-Yan Zhu may publish in the future.
Co-authorship network of co-authors of Jun-Yan Zhu
This figure shows the co-authorship network connecting the top 25 collaborators of Jun-Yan Zhu.
A scholar is included among the top collaborators of Jun-Yan Zhu 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 Jun-Yan Zhu. Jun-Yan Zhu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Deng, Kangle, Andrew Liu, Jun-Yan Zhu, & Deva Ramanan. (2022). Depth-supervised NeRF: Fewer Views and Faster Training for Free. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 12872–12881.459 indexed citations breakdown →
Park, Taesung, Jun-Yan Zhu, Oliver Wang, et al.. (2020). Swapping Autoencoder for Deep Image Manipulation. Neural Information Processing Systems. 33. 7198–7211.13 indexed citations
12.
Bau, David, Jun-Yan Zhu, Hendrik Strobelt, et al.. (2019). Visualizing and Understanding Generative Adversarial Networks (Extended Abstract).. arXiv (Cornell University).2 indexed citations
Sundaram, Subramanian, Petr Kellnhofer, Yunzhu Li, et al.. (2019). Learning the signatures of the human grasp using a scalable tactile glove. Nature. 569(7758). 698–702.880 indexed citations breakdown →
15.
Zhu, Jun-Yan, Zhoutong Zhang, Chengkai Zhang, et al.. (2018). Visual Object Networks: Image Generation with Disentangled 3D Representations. Neural Information Processing Systems. 31. 118–129.66 indexed citations
16.
Zhu, Jun-Yan, Richard Zhang, Deepak Pathak, et al.. (2017). Multimodal Image-to-Image Translation by Enforcing Bi-Cycle Consistency. Neural Information Processing Systems. 465–476.20 indexed citations
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.