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Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs


Nov 27, 2022
Guangrun Wang, Philip H. S. Torr

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* This paper has 29 pages with 22 figures, including rich supplementary information 

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Structure-Preserving 3D Garment Modeling with Neural Sewing Machines


Nov 12, 2022
Xipeng Chen, Guangrun Wang, Dizhong Zhu, Xiaodan Liang, Philip H. S. Torr, Liang Lin

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* NeurIPS 2022 

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Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers


Oct 16, 2022
Tao Tang, Changlin Li, Guangrun Wang, Kaicheng Yu, Xiaojun Chang, Xiaodan Liang

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Understanding Weight Similarity of Neural Networks via Chain Normalization Rule and Hypothesis-Training-Testing


Aug 08, 2022
Guangcong Wang, Guangrun Wang, Wenqi Liang, Jianhuang Lai

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* Weight Similarity of Neural Networks 

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Beyond Fixation: Dynamic Window Visual Transformer


Apr 08, 2022
Pengzhen Ren, Changlin Li, Guangrun Wang, Yun Xiao, Qing Du, Xiaodan Liang, Xiaojun Chang

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* CVPR2022 

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Automated Progressive Learning for Efficient Training of Vision Transformers


Mar 28, 2022
Changlin Li, Bohan Zhuang, Guangrun Wang, Xiaodan Liang, Xiaojun Chang, Yi Yang

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* Accepted to CVPR 2022 

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DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and Transformers


Sep 21, 2021
Changlin Li, Guangrun Wang, Bing Wang, Xiaodan Liang, Zhihui Li, Xiaojun Chang

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* Extension of the CVPR 2021 oral paper (https://openaccess.thecvf.com/content/CVPR2021/html/Li_Dynamic_Slimmable_Network_CVPR_2021_paper.html

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EfficientBERT: Progressively Searching Multilayer Perceptron via Warm-up Knowledge Distillation


Sep 16, 2021
Chenhe Dong, Guangrun Wang, Hang Xu, Jiefeng Peng, Xiaozhe Ren, Xiaodan Liang

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* Findings of EMNLP 2021 

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Pi-NAS: Improving Neural Architecture Search by Reducing Supernet Training Consistency Shift


Aug 22, 2021
Jiefeng Peng, Jiqi Zhang, Changlin Li, Guangrun Wang, Xiaodan Liang, Liang Lin

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* Accepted to ICCV 2021 

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Solving Inefficiency of Self-supervised Representation Learning


Apr 18, 2021
Guangrun Wang, Keze Wang, Guangcong Wang, Phillip H. S. Torr, Liang Lin

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* 11 pages, 3 figures 

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