YUNHE
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Yunhe Wang

I am a senior researcher at Huawei Noah's Ark Lab, Beijing, 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

News

  • 06/2020, two papers have been accepted by ICML 2020.
  • 02/2020, seven papers have been accepted by CVPR 2020.
  • 01/2020, one paper has been accepted by IEEE TNNLS.
  • 11/2019, three papers have been accepted by AAAI 2020.
  • 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).

  • 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

  • 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 using bilibili.
  • Research

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

    Conference Papers:

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

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

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

    4. 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

    5. 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

    6. 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

    7. 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

    8. 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

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

    10. 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

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

    12. 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

    13. 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

    14. 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

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

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

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

    18. 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

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

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

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

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

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

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

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

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

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

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

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

    30. 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. Learning student networks via feature embedding
      Hanting Chen, Yunhe Wang, Chang Xu, Chao Xu, Dacheng Tao
      IEEE TNNLS 2020 | paper

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

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

    4. DCT inspired feature transform for image retrieval and reconstruction
      Yunhe Wang, Miaojing Shi, Shan You, Chao Xu
      IEEE TIP 2016 | paper

    Services

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

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

  • Program Committee Members of 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

  • 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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