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).

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.

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

2. 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)

3. 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)

4. 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

5. Scientific Control for Reliable Neural Network Pruning
   Yehui Tang, Yunhe Wang, Yixing Xu, Dacheng Tao, Chunjing Xu, Chao Xu, Chang Xu
   NeurIPS 2020

6. Adapting Neural Architectures Between Domains
   Yanxi Li, Zhaohui Yang, Yunhe Wang, Chang Xu
   NeurIPS 2020

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

8. 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

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

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

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

12. 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

13. 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
    

14. 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

15. 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

16. 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

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

18. 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

19. 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

20. 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

21. 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

22. 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

23. 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

24. 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

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

26. 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

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

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

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

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

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

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

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

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

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

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

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

38. 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. Adversarial Recurrent Time Series Imputation
   Shuo Yang, Minjing Dong, Yunhe Wang, Chang Xu
   IEEE TNNLS 2020 | to appear

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

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

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

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

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