Mingguang Li

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).

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.

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. 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. 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

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.