Yunhe Wang

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.

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

Awards