YUNHE
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Yunhe Wang

I am now the head of the Huawei Applied AI lab and also a senior researcher at Huawei Noah's Ark Lab, where I work on deep learning, model compression, and computer vision, etc. Before that, I did my PhD at school of EECS, Peking University, where I was co-advised by Prof. Chao Xu and Prof. Dacheng Tao. I did my bachelors at school of science, Xidian University.

Email  /  Google Scholar  /  Zhi Hu  /  DBLP

News

  • 12/2023, We recently developed PanGu-π: an enhanced LLM architecture via Nonlinearity Compensation.

  • 05/2023, I will give a talk about Multimodal Learning at VALSE 2023 workshop.

  • 04/2023, I will give a talk about Robust Machine Learning at The CCF Advanced Disciplines Lectures.

  • 03/2023, I accepted the invitation to serve as an Area Chair for NeurIPS 2023.

  • 03/2023, 4 papers have been accepted by CVPR 2023.

  • 12/2022, I accepted the invitation to serve as an Area Chair for ICML 2023.

  • 12/2022, I will give a talk about Efficient Deep Networks at China National Computer Congress (CNCC) 2022.

  • 09/2022, 9 papers have been accepted by NeurIPS 2022.

  • 05/2022, I will give a talk about Vision Transformer at BAAI 2022.

  • 02/2022, 8 papers have been accepted by CVPR 2022.

  • 02/2022, Our suvery paper on vision transformer has been accepted by IEEE TPAMI.

    • Recent Projects

    Actually, model compression is a kind of technique for developing portable deep neural networks with lower memory and computation costs. I have done several projects in Huawei including some smartphones' applications in 2019 and 2020 (e.g. Mate 30 and Honor V30). Currently, I am leading the AdderNet project, which aims to develop a series of deep learning models using only additions (Discussions on Reddit).

  • PanGU-π

    • Paper

    Introducing PanGu-π, a new architecture for Large Language Model. As the world of Large Language Models (LLMs) continues to evolve with larger models and datasets for enhanced performance, the critical aspect of LLM architecture improvement often remains overlooked. PanGu-π addresses this gap by introducing modules that significantly enhance nonlinearity, thereby greatly boosting the model's expressive capabilities. Achieving leading performance and efficiency in both 7B and 1B model scales, PanGu-π is a testament to the power of architectural innovation in LLMs. Further extending its impact, the specialized YunShan model is making waves in high-value domains such as finance and law, showcasing the practical and powerful application of this groundbreaking technology.

  • The Vanilla Neural Architecture for the 2020s

    • Project Page | Paper | Discussion on Zhihu

    VanillaNet is remarkable! The concept was born from embracing the "less is more" philosophy in computer vision. It's elegantly designed by avoiding intricate depth and operations, such as self-attention, making it remarkably powerful yet concise. The 6-layer VanillaNet surpasses ResNet-34, and the 13-layer variant achieves about 83% Top-1 accuracy, outpacing the performance of networks with hundreds of layers, and revealing exceptional hardware efficiency advantages.

  • Adder Neural Networks

    • Project Page | Hardware Implementation

    I would like to say, AdderNet is very cool! The initial idea was came up in about 2017 when climbing with some friends at Beijing. By replacing all convolutional layers (except the first and the last layers), we now can obtain comparable performance on ResNet architectures. In addition, to make the story more complete, we recent release the hardware implementation and some quantization methods. The results are quite encouraging, we can reduce both the energy consumption and thecircuit areas significantly without affecting the performance. Now, we are working on more applications to reduce the costs of launching AI algorithms such as low-level vision, detection, and NLP tasks.

  • GhostNet on MindSpore: SOTA Lightweight CV Networks

    • Huawei Connect (HC) 2020 | MindSpore Hub

    The initial verison of GhostNet was accepted by CVPR 2020, which achieved SOTA performance on ImageNet: 75.7% top1 acc with only 226M FLOPS. In the current version, we release a series computer vision models (e.g. int8 quantization, detection, and larger networks) on MindsSpore 1.0 and Mate 30 Pro (Kirin 990).

  • AI on Ascend: Real-Time Video Style Transfer

    •   

    Huawei Developer Conference (HDC) 2020 | Online Demo

    This project aims to develop a video style transfer system on the Huawei Atlas 200 DK AI developer Kit. The latency of the original model for processing one image is about 630ms. After accelerating it using our method, the lantency now is about 40ms.

    Talks

  • 12/2022, Hardware Efficient Deep Learning at China National Computer Congress (CNCC) 2022. Thanks Prof. Jian Cheng for the invitation.

  • 05/2022, Low-Level Vision Transformer and Model Compression at BAAI Conference 2022. Thanks Prof. Shiguang Shan for the invitation.

  • 10/2021, Vision Transformer at VALSE 2021 Tutorial. Thanks Prof. Shiguang Shan for the invitation.

  • 05/2021, Adder Neural Network at HAET ICLR 2021 workshop. Thanks Vahid Partovi Nia for the invitation.

  • 06/2020, "AI on the Edge - Discussion on the Gap Between Industry and Academia" at VALSE Webinar.

  • 05/2020, "Edge AI: Progress and Future Directions" at QbitAI.

    • Research

    I'm interested in devleoping efficient models for computer vision (e.g. classification, detection, and super-resolution) using pruning, quantization, distilaltion, NAS, etc.

    Preprint Papers:

    1. PanGu-π: Enhancing Language Model Architectures via Nonlinearity Compensation
      Yunhe Wang, Hanting Chen, Yehui Tang, Tianyu Guo, Kai Han, Ying Nie, Xutao Wang, Hailin Hu, Zheyuan Bai, Yun Wang, Fangcheng Liu, Zhicheng Liu, Jianyuan Guo, Sinan Zeng, Yinchen Zhang, Qinghua Xu, Qun Liu, Jun Yao, Chao Xu, Dacheng Tao
      arXiv 2023.12.xx submitted | paper

    Conference Papers:

    1. Accelerating Sparse Convolution with Column Vector-Wise Sparsity
      Yijun Tan, Kai Han, Kang Zhao, Xianzhi Yu, Zidong Du, Yunji Chen, Yunhe Wang, Jun Yao
      NeurIPS 2022 | paper

    2. Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation
      Zhiwei Hao, Jianyuan Guo, Ding Jia, Kai Han, Yehui Tang, Chao Zhang, Han Hu, Yunhe Wang
      NeurIPS 2022 | paper

    3. A Transformer-Based Object Detector with Coarse-Fine Crossing Representations
      Zhishan Li, Ying Nie, Kai Han, Jianyuan Guo, Lei Xie, Yunhe Wang
      NeurIPS 2022 | paper | MindSpore code

    4. Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor
      Yuqiao Liu, Yehui Tang, Zeqiong Lv, Yunhe Wang, Yanan Sun
      NeurIPS 2022 | paper | code | MindSpore code

    5. Random Normalization Aggregation for Adversarial Defense
      Minjing Dong, Xinghao Chen, Yunhe Wang, Chang Xu
      NeurIPS 2022 | paper | code | MindSpore code

    6. Redistribution of Weights and Activations for AdderNet Quantization
      Ying Nie, Kai Han, Haikang Diao, Chuanjian Liu, Enhua Wu, Yunhe Wang
      NeurIPS 2022 | paper | MindSpore code

    7. BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons
      Yixing Xu, Xinghao Chen, Yunhe Wang
      NeurIPS 2022 | paper | MindSpore code | Spotlight

    8. GhostNetV2: Enhance Cheap Operation with Long-Range Attention
      Yehui Tang, Kai Han, Jianyuan Guo, Chang Xu, Chao Xu, Yunhe Wang
      NeurIPS 2022 | paper | code | Spotlight

    9. Vision GNN: An Image is Worth Graph of Nodes
      Kai Han*, Yunhe Wang*, Jianyuan Guo, Yehui Tang, Enhua Wu
      NeurIPS 2022 (* equal contribution) | paper | code | MindSpore code

    10. Spatial-Channel Token Distillation for Vision MLPs
      Yanxi Li, Xinghao Chen, Minjing Dong, Yehui Tang, Yunhe Wang, Chang Xu
      ICML 2022 | paper

    11. Federated Learning with Positive and Unlabeled Data
      Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping Deng, Yunhe Wang
      ICML 2022 | paper

    12. Brain-inspired Multilayer Perceptron with Spiking Neurons
      Wenshuo Li, Hanting Chen, Jianyuan Guo, Ziyang Zhang, Yunhe Wang
      CVPR 2022 | paper | MindSpore code

    13. Source-Free Domain Adaptation via Distribution Estimation
      Ning Ding, Yixing Xu, Yehui Tang, Chao Xu, Yunhe Wang, Dacheng Tao
      CVPR 2022 | paper

    14. Multimodal Token Fusion for Vision Transformers
      Yikai Wang, Xinghao Chen, Lele Cao, Wenbing Huang, Fuchun Sun, Yunhe Wang
      CVPR 2022 | paper | code | MindSpore code

    15. An Image Patch is a Wave: Phase-Aware Vision MLP
      Yehui Tang, Kai Han, Jianyuan Guo, Chang Xu, Yanxi Li, Chao Xu, Yunhe Wang
      CVPR 2022 | paper | code | Oral Presentation

    16. Instance-Aware Dynamic Neural Network Quantization
      Zhenhua Liu, Yunhe Wang, Kai Han, Siwei Ma, Wen Gao
      CVPR 2022 | paper | code | MindSpore code | Oral Presentation

    17. Hire-MLP: Vision MLP via Hierarchical Rearrangement
      Jianyuan Guo, Yehui Tang, Kai Han, Xinghao Chen, Han Wu, Chao Xu, Chang Xu, Yunhe Wang
      CVPR 2022 | paper

    18. CMT: Convolutional Neural Networks Meet Vision Transformers
      Jianyuan Guo, Kai Han, Han Wu, Yehui Tang, Xinghao Chen, Yunhe Wang, Chang Xu
      CVPR 2022 | paper

    19. Patch Slimming for Efficient Vision Transformers
      Yehui Tang, Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chao Xu, Dacheng Tao
      CVPR 2022 | paper

    20. Transformer in Transformer
      Kai Han, An Xiao, Enhua Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang
      NeurIPS 2021 | paper | code | MindSpore code

    21. Learning Frequency Domain Approximation for Binary Neural Networks
      Yixing Xu, Kai Han, Chang Xu, Yehui Tang, Chunjing Xu, Yunhe Wang
      NeurIPS 2021 | paper | Oral Presentation

    22. Dynamic Resolution Network
      Mingjian Zhu*, Kai Han*, Enhua Wu, Qiulin Zhang, Ying Nie, Zhenzhong Lan, Yunhe Wang
      NeurIPS 2021 (* equal contribution) | paper

    23. Post-Training Quantization for Vision Transformer
      Zhenhua Liu, Yunhe Wang, Kai Han, Wei Zhang, Siwei Ma, Wen Gao
      NeurIPS 2021 | paper

    24. Augmented Shortcuts for Vision Transformers
      Yehui Tang, Kai Han, Chang Xu, An Xiao, Yiping Deng, Chao Xu, Yunhe Wang
      NeurIPS 2021 | paper

    25. Adder Attention for Vision Transformer
      Han Shu*, Jiahao Wang*, Hanting Chen, Lin Li, Yujiu Yang, Yunhe Wang
      NeurIPS 2021 (* equal contribution) | paper

    26. Towards Stable and Robust Addernets
      Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu
      NeurIPS 2021 | paper

    27. Handling Long-Tailed Feature Distribution in Addernets
      Minjing Dong, Yunhe Wang, Xinghao Chen, Chang Xu
      NeurIPS 2021 | paper

    28. Neural Architecture Dilation for Adversarial Robustness
      Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu
      NeurIPS 2021 | paper

    29. An Empirical Study of Adder Neural Networks for Object Detection
      Xinghao Chen, Chang Xu, Minjing Dong, Chunjing Xu, Yunhe Wang
      NeurIPS 2021 | paper

    30. Learning Frequency-Aware Dynamic Network for Efficient Super-Resolution
      Wenbin Xie, Dehua Song, Chang Xu, Chunjing Xu, Hui Zhang, Yunhe Wang
      ICCV 2021 | paper

    31. Winograd Algorithm for AdderNet
      Wenshuo Li, Hanting Chen, Mingqiang Huang, Xinghao Chen, Chunjing Xu, Yunhe Wang
      ICML 2021 | paper

    32. Distilling Object Detectors via Decoupled Features
      Jianyuan Guo, Kai Han, Yunhe Wang, Wei Zhang, Chunjing Xu, Chang Xu
      CVPR 2021 | paper

    33. HourNAS: Extremely Fast Neural Architecture Search Through an Hourglass Lens
      Zhaohui Yang, Yunhe Wang, Xinghao Chen, Jianyuan Guo, Wei Zhang,
      Chao Xu, Chunjing Xu, Dacheng Tao, Chang Xu
      CVPR 2021 | paper | MindSpore code

    34. Manifold Regularized Dynamic Network Pruning
      Yehui Tang, Yunhe Wang, Yixing Xu, Yiping Deng, Chao Xu, Dacheng Tao, Chang Xu
      CVPR 2021 | paper | MindSpore code

    35. Learning Student Networks in the Wild
      Hanting Chen, Tianyu Guo, Chang Xu, Wenshuo Li, Chunjing Xu, Chao Xu, Yunhe Wang
      CVPR 2021 | paper

    36. AdderSR: Towards Energy Efficient Image Super-Resolution
      Dehua Song*, Yunhe Wang*, Hanting Chen, Chang Xu, Chunjing Xu, Dacheng Tao
      CVPR 2021 (* equal contribution) | paper | code | Oral Presentation

    37. ReNAS: Relativistic Evaluation of Neural Architecture Search
      Yixing Xu, Yunhe Wang, Kai Han, Yehui Tang, Shangling Jui, Chunjing Xu, Chang Xu
      CVPR 2021 | paper | MindSpore code | Oral Presentation

    38. Pre-Trained Image Processing Transformer
      Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu,
      Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao
      CVPR 2021 | paper | MindSpore code | Pytorch code

    39. Data-Free Knowledge Distillation For Image Super-Resolution
      Yiman Zhang, Hanting Chen, Xinghao Chen, Yiping Deng, Chunjing Xu, Yunhe Wang
      CVPR 2021 | paper

    40. Positive-Unlabeled Data Purification in the Wild for Object Detection
      Jianyuan Guo, Kai Han, Han Wu, Xinghao Chen, Chao Zhang, Chunjing Xu, Chang Xu, Yunhe Wang
      CVPR 2021 | paper

    41. One-shot Graph Neural Architecture Search with Dynamic Search Space
      Yanxi Li, Zean Wen, Yunhe Wang, Chang Xu
      AAAI 2021 paper

    42. Adversarial Robustness through Disentangled Representations
      Shuo Yang, Tianyu Guo, Yunhe Wang, Chang Xu
      AAAI 2021 paper

    43. Kernel Based Progressive Distillation for Adder Neural Networks
      Yixing Xu, Chang Xu, Xinghao Chen, Wei Zhang, Chunjing Xu, Yunhe Wang
      NeurIPS 2020 | paper | code | Spotlight

    44. Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets
      Kai Han*, Yunhe Wang*, Qiulin Zhang, Wei Zhang, Chunjing Xu, Tong Zhang
      NeurIPS 2020 (* equal contribution) | paper | code

    45. Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts
      Guilin Li*, Junlei Zhang*, Yunhe Wang, Chuanjian Liu, Matthias Tan, Yunfeng Lin,
      Wei Zhang, Jiashi Feng, Tong Zhang
      NeurIPS 2020 (* equal contribution) | paper | code

    46. Searching for Low-Bit Weights in Quantized Neural Networks
      Zhaohui Yang, Yunhe Wang, Kai Han, Chunjing Xu, Chao Xu, Dacheng Tao, Chang Xu
      NeurIPS 2020 | paper | code

    47. SCOP: Scientific Control for Reliable Neural Network Pruning
      Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing Xu, Chao Xu, Chang Xu
      NeurIPS 2020 | paper | code

    48. Adapting Neural Architectures Between Domains
      Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu
      NeurIPS 2020 | paper | code

    49. Discernible Image Compression
      Zhaohui Yang, Yunhe Wang, Chang Xu, Peng Du, Chao Xu, Chunjing Xu, Qi Tian
      ACM MM 2020 | paper

    50. Optical Flow Distillation: Towards Efficient and Stable Video Style Transfer
      Xinghao Chen*, Yiman Zhang*, Yunhe Wang, Han Shu, Chunjing Xu, Chang Xu
      ECCV 2020 (* equal contribution) | paper | code

    51. Learning Binary Neurons with Noisy Supervision
      Kai Han, Yunhe Wang, Yixing Xu, Chunjing Xu, Enhua Wu, Chang Xu
      ICML 2020 | paper

    52. Neural Architecture Search in a Proxy Validation Loss Landscape
      Yanxi Li, Minjing Dong, Yunhe Wang, Chang Xu
      ICML 2020 | paper

    53. On Positive-Unlabeled Classification in GAN
      Tianyu Guo, Chang Xu, Jiajun Huang, Yunhe Wang, Boxin Shi, Chao Xu, Dacheng Tao
      CVPR 2020 | paper

    54. CARS: Continuous Evolution for Efficient Neural Architecture Search
      Zhaohui Yang, Yunhe Wang, Xinghao Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu
      CVPR 2020 | paper | code

    55. AdderNet: Do We Really Need Multiplications in Deep Learning?
      Hanting Chen*, Yunhe Wang*, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu
      CVPR 2020 (* equal contribution) | paper | code | Oral Presentation

    56. A Semi-Supervised Assessor of Neural Architectures
      Yehui Tang, Yunhe Wang, Yixing Xu, Hanting Chen, Boxin Shi, Chao Xu, Chunjing Xu, Qi Tian, Chang Xu
      CVPR 2020 | paper

    57. Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
      Jianyuan Guo, Kai Han, Yunhe Wang, Chao Zhang, Zhaohui Yang, Han Wu, Xinghao Chen, Chang Xu
      CVPR 2020 | paper | code

    58. Frequency Domain Compact 3D Convolutional Neural Networks
      Hanting Chen, Yunhe Wang, Han Shu, Yehui Tang, Chunjing Xu, Boxin Shi, Chao Xu, Qi Tian, Chang Xu
      CVPR 2020 | paper

    59. GhostNet: More Features from Cheap Operations
      Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu
      CVPR 2020 | paper | code

    60. Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks
      Yehui Tang, Yunhe Wang, Yixing Xu, Boxin Shi, Chao Xu, Chunjing Xu, Chang Xu
      AAAI 2020 | paper | code

    61. DropNAS: Grouped Operation Dropout for Differentiable Architecture Search
      Weijun Hong, Guilin Li, Weinan Zhang, Ruiming Tang, Yunhe Wang, Zhenguo Li, Yong Yu
      IJCAI 2020 | paper

    62. Distilling Portable Generative Adversarial Networks for Image Translation
      Hanting Chen, Yunhe Wang, Han Shu, Changyuan Wen, Chunjing Xu, Boxin Shi, Chao Xu, Chang Xu
      AAAI 2020 | paper

    63. Efficient Residual Dense Block Search for Image Super-Resolution
      Dehua Song, Chang Xu, Xu Jia, Yiyi Chen, Chunjing Xu, Yunhe Wang
      AAAI, 2020 | paper | code

    64. Positive-Unlabeled Compression on the Cloud
      Yixing Xu, Yunhe Wang, Hanting Chen, Kai Han, Chunjing Xu, Dacheng Tao, Chang Xu
      NeurIPS 2019 | paper | code | supplement

    65. Data-Free Learning of Student Networks
      Hanting Chen,Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi,
      Chunjing Xu, Chao Xu, Qi Tian
      ICCV 2019 | paper | code

    66. Co-Evolutionary Compression for Unpaired Image Translation
      Han Shu, Yunhe Wang, Xu Jia, Kai Han, Hanting Chen, Chunjing Xu, Qi Tian, Chang Xu
      ICCV 2019 | paper | code

    67. LegoNet: Efficient Convolutional Neural Networks with Lego Filters
      Zhaohui Yang, Yunhe Wang, Hanting Chen, Chuanjian Liu, Boxin Shi, Chao Xu, Chunjing Xu, Chang Xu
      ICML 2019 | paper | code

    68. Learning Instance-wise Sparsity for Accelerating Deep Models
      Chuanjian Liu, Yunhe Wang, Kai Han, Chunjing Xu, Chang Xu
      IJCAI 2019 | paper

    69. Attribute Aware Pooling for Pedestrian Attribute Recognition
      Kai Han, Yunhe Wang, Han Shu, Chuanjian Liu, Chunjing Xu, Chang Xu
      IJCAI 2019 | paper

    70. Crafting Efficient Neural Graph of Large Entropy
      Minjing Dong, Hanting Chen, Yunhe Wang, Chang Xu
      IJCAI 2019 | paper

    71. Low Resolution Visual Recognition via Deep Feature Distillation
      Mingjian Zhu, Kai Han, Chao Zhang, Jinlong Lin, Yunhe Wang
      ICASSP 2019 | paper

    72. Learning Versatile Filters for Efficient Convolutional Neural Networks
      Yunhe Wang, Chang Xu, Chunjing Xu, Chao Xu, Dacheng Tao
      NeurIPS 2018 | paper | code | supplement

    73. Towards Evolutionary Compression
      Yunhe Wang, Chang Xu, Jiayan Qiu, Chao Xu, Dacheng Tao
      SIGKDD 2018 | paper

    74. Autoencoder Inspired Unsupervised Feature Selection
      Kai Han, Yunhe Wang, Chao Zhang, Chao Li, Chao Xu
      ICASSP 2018 | paper | code

    75. Adversarial Learning of Portable Student Networks
      Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao
      AAAI 2018 | paper

    76. Beyond Filters: Compact Feature Map for Portable Deep Model
      Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao
      ICML 2017 | paper | code | supplement

    77. Beyond RPCA: Flattening Complex Noise in the Frequency Domain
      Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao
      AAAI 2017 | paper

    78. Privileged Multi-Label Learning
      Shan You, Chang Xu, Yunhe Wang, Chao Xu, Dacheng Tao
      IJCAI 2017 | paper

    79. CNNpack: Packing Convolutional Neural Networks in the Frequency Domain
      Yunhe Wang, Chang Xu, Shan You, Chao Xu, Dacheng Tao
      NeurIPS 2016 | paper | supplement

    Journal Papers:

    1. Neural Architecture Search via Proxy Validation
      Yanxi Li, Minjing Dong, Yunhe Wang, Chang Xu
      IEEE TPAMI 2022 | paper

    2. Local Means Binary Networks for Image Super-Resolution
      Keyu Li, Nannan Wang, Jingwei Xin, Xinrui Jiang, Jie Li, Xinbo Gao, Kai Han, Yunhe Wang
      IEEE TNNLS 2022 | paper

    3. GhostNets on Heterogeneous Devices via Cheap Operations
      Kai Han, Yunhe Wang, Chang Xu, Jianyuan Guo, Chunjing Xu, Enhua Wu, Qi Tian
      IJCV 2022 | paper | code

    4. A Survey on Visual Transformer
      Kai Han, Yunhe Wang, Hanting Chen, Xinghao Chen, Jianyuan Guo, Zhenhua Liu, Yehui Tang, An Xiao, Chunjing Xu, Yixing Xu, Zhaohui Yang, Yiman Zhang, Dacheng Tao
      IEEE TPAMI 2022 | paper

    5. Learning Versatile Convolution Filters for Efficient Visual Recognition
      Kai Han*, Yunhe Wang*, Chang Xu, Chunjing Xu, Enhua Wu, Dacheng Tao
      IEEE TPAMI 2021 (* equal contribution) | paper | code

    6. Adversarial Recurrent Time Series Imputation
      Shuo Yang, Minjing Dong, Yunhe Wang, Chang Xu
      IEEE TNNLS 2020 |paper

    7. Learning Student Networks via Feature Embedding
      Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao
      IEEE TNNLS 2020 | paper

    8. Packing Convolutional Neural Networks in the Frequency Domain
      Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao
      IEEE TPAMI 2018 | paper

    9. DCT Regularized Extreme Visual Recovery
      Yunhe Wang, Chang Xu, Shan You, Chao Xu, Dacheng Tao
      IEEE TIP 2017 | paper

    10. DCT Inspired Feature Transform for Image Retrieval and Reconstruction
      Yunhe Wang, Miaojing Shi, Shan You, Chao Xu
      IEEE TIP 2016 | paper

    Workshop Papers:

    1. PyramidTNT: Improved Transformer-in-Transformer Baselines with Pyramid Architecture
      Kai Han, Jianyuan Guo, Yehui Tang, Yunhe Wang
      CVPR Workshop 2022 | paper | code

    2. Network Amplification with Efficient MACs Allocation
      Chuanjian Liu, Kai Han, An Xiao, Ying Nie, Wei Zhang, Yunhe Wang
      CVPR Workshop 2022 | paper

    3. Searching for Energy-Efficient Hybrid Adder-Convolution Neural Networks
      Wenshuo Li, Xinghao Chen, Jinyu Bai, Xuefei Ning, Yunhe Wang
      CVPR Workshop 2022 | paper

    4. Searching for Accurate Binary Neural Architectures
      Mingzhu Shen, Kai Han, Chunjing Xu, Yunhe Wang
      ICCV Neural Architectures Workshop 2019 | paper

    Services

  • Area Chair of NeurIPS 2023, ICML 2023, NeurIPS 2022, ICML 2021, NeurIPS 2021.

  • Action Editor of TMLR (Transactions on Machine Learning Research).

  • Senior Program Committee Members of IJCAI 2021, IJCAI 2020 and IJCAI 2019.

  • Journal Reviewers of IEEE T-PAMI, IJCV, IEEE T-IP, IEEE T-NNLS, IEEE T-MM, IEEE T-KDE, etc.

  • Program Committee Members of ICCV 2021, AAAI 2021, ICLR 2021, NeurIPS 2020, ICML 2020, ECCV 2020, CVPR 2020, ICLR 2020, AAAI 2020, ICCV 2019, CVPR 2019, ICLR 2019, AAAI 2019, IJCAI 2018, AAAI 2018, NeurIPS 2018, etc.

    • Awards

  • 2020, Nomination for Outstanding Youth Paper Award, WAIC.

  • 2017, Google PhD Fellowship.

  • 2017, Baidu Scholarship.

  • 2017, President's PhD Scholarship, Peking University.

  • 2017, National Scholarship for Graduate Students.

  • 2016, National Scholarship for Graduate Students.

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