Chenshuang Zhang
I am currently a Ph.D. student advised by Professor In So Kweon and Professor Junmo Kim, from School of Electronic Engineering, KAIST. My current research interest lies in the interaction of computer vision and machine learning, including but not limited to:
- Generative models
- Multi-modality learning
- Adversarial machine learning
Before joining KAIST, I worked as a Senior Machine Learning Engineer at Alibaba Group, Beijing, China.
I obtained my master's degree from Tsinghua University, working on deep learning in medical applications.
I'm always open to collaborations on various research topics. If you are interested in my work, please feel free to contact me.
Email  / 
Google Scholar  / 
LinkedIn
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Selected Publications
* indicates equal contributions
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ImageNet-D: Benchmarking Neural Network Robustness on Diffusion Synthetic Object
Chenshuang Zhang, Fei Pan, Junmo Kim, In So Kweon, Chengzhi Mao
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
CVPR Highlight (2.8%)
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Towards Understanding Dual BN In Hybrid Adversarial Training
Chenshuang Zhang, Chaoning Zhang, Kang Zhang, Axi Niu, Junmo Kim, In So Kweon
Transactions on Machine Learning Research (TMLR), 2024
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Text-to-image Diffusion Models in Generative AI: A Survey
Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, Junmo Kim, In So Kweon
Under review, 2024
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A survey on masked autoencoder for visual self-supervised learning
Chaoning Zhang, Chenshuang Zhang, Junha Song, John Seon Keun Yi, In So Kweon
International Joint Conference on Artificial Intelligence (IJCAI), 2023
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Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness
Chaoning Zhang*, Kang Zhang*, Chenshuang Zhang, Axi Niu, Jiu Feng, Chang D Yoo, In So Kweon
European Conference on Computer Vision (ECCV), 2022
ECCV Oral (2.3%)
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How does simsiam avoid collapse without negative samples? a unified understanding with self-supervised contrastive learning
Chaoning Zhang*, Kang Zhang*, Chenshuang Zhang, Trung X Pham, Chang D Yoo, In So Kweon
International Conference on Learning Representations (ICLR), 2022
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A global and updatable ECG beat classification system based on recurrent neural networks and active learning
Guijin Wang, Chenshuang Zhang (First student author), Yongpan Liu, Huazhong Yang, Dapeng Fu, Haiqing Wang, Ping Zhang
Information Sciences (Impact factor 8.23), 2019
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Bidirectional recurrent neural network and convolutional neural network (BiRCNN) for ECG beat classification
Pengwei Xie, Guijin Wang, Chenshuang Zhang, Ming Chen, Huazhong Yang, Tingting Lv, Zhenhua Sang, Ping Zhang
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
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Patient-specific ECG classification based on recurrent neural networks and clustering technique
Chenshuang Zhang, Guijin Wang, Jingwei Zhao, Pengfei Gao, Jianping Lin, Huazhong Yang
2017 13th IASTED International Conference on Biomedical Engineering (BioMed), 2017
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Thanks to Jon Barron for sharing the code of the nice website.
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