Hao Dong

董豪 助理教授(博导)北京大学-信息科学技术学院-前沿计算研究中心

hao.dong [at] pku.edu.cn

Google Scholar / Research Gate / DBLP / GitHub

About Me

I am an assistant professor in CFCS-EECS at Peking University. I obtained my Ph.D. degree from Imperial College London under the supervision of Yike Guo in Fall 2019. My current research involves deep learning and computer vision with the goal of reducing the data required for learning intelligent systems. I am passionate about popularising artificial intelligence technologies and established TensorLayer, a deep learning and reinforcement learning library for scientists and engineers, which won the Best Open Source Software Award at ACM Multimedia 2017. Before Ph.D., I received a MSc specialist degree with distinction from Imperial, and a first-class BEng degree from the University of Central Lancashire. I founded a startup for digital healthcare with Yike Guo between 2012 and 2014.


  • [08/2020] We won the CityLearn Challenge 2020, reducing 13% cost of building energy
  • [08/2020] TensorLayer 3.0.0 will supports multiple backends, such as TensorFlow, MindSpore and more, supporting GPU and Huawei-Ascend. Stay tuned!
  • [07/2020] Our DRL book is published!
  • [08/2019] I graduated from ICL and joined PKU.
  • [06/2019] Release RL Model Zoo for teaching and research.
  • [05/2019] Release TensorLayer 2.0 ! A BIG Updated!
  • [12/2018] TensorLayer give a talk at Google Developer Groups (GDG) DevFest. London, Dec 1 2018
  • [01/2018] Published my 1st Chinese Deep Learning Book
  • [10/2017] We won the Best Open Source Software Award @ACM MM 2017
  • [09/2016] TensorLayer is released. It has quickly gained over 2000+ stars on Github!

  • Teaching

  • Introduction to Computing (Fall Term 2020)
  • Deep Generative Models (Spring Term 2020)
  • Study and Practice on Topics of Frontier Computing (I) (Autumn Term 2019)
  • Introduction to Deep Learning (Turing Class) (Summer Term 2019)

  • Services

  • Organiser of TensorLayer community
  • Reviewer of CoRL(20), SIGGRAPH Asia(20), MICCAI(20), IROS(20), China CAD&CG(20), EuroGRAPHICS(20), PAMI(19), SIGGRAPH(19), TIP(18), TKDE(18), Neurocomputing(17), PLUS ONE(18)

  • Books
    Deep Reinforcement Learning: Fundamentals, Research and Applications
    Hao Dong, Zihan Ding, Shanghang Zhang Eds.
    Springer 2020 ISBN 978-981-15-4094-3, 1st ed..
    [Homepage] [Springer]
    Deep Learning using TensorLayer (深度学习:一起玩转TensorLayer)
    Hao Dong, Yike Guo, Guang Yang et al
    Publishing House of Electronics Industry 2018 ISBN: 9787121326226.
    Survey on Feature Extraction and Applications of Biosignals
    Akara Supratak, Chao Wu, Hao Dong, Kai Sun, Yike Guo
    Machine Learning for Health Informatics, Springer, Page 161-182 2016.
    Recent Papers
    Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control
    Qingrui Zhang, Hao Dong and Wei Pan
    Int. Conf. on Distributed Artificial Intelligence (DAI) 2020 (Oral).
    Generative 3D Part Assembly via Dynamic Graph Learning
    Jialei Huang*, Guanqi Zhan*, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas Guibas, Hao Dong
    arXiv-2006.07793 2020.
    Role-Wise Data Augmentation for Knowledge Distillation
    Jie Fu, Xue Geng, Zhijian Duan, Bohan Zhuang, Xingdi Yuan, Adam Trischler, Jie Lin, Chris Pal, Hao Dong
    arXiv-2004.08861 2020.
    Unpaired Image-to-Image Translation using Adversarial Consistency Loss
    Yihao Zhao, Ruihai Wu, Hao Dong
    European Conference on Computer Vision (ECCV) 2020.
    [Paper] [Code]
    Before 2020
    DLGAN: Disentangling Label-Specific Fine-Grained Features for Image Manipulation
    Guanqi Zhan, Yihao Zhao, Bingchan Zhao, Haoqi Yuan, Baoquan Chen, Hao Dong
    arXiv-1911.09943 2019.
    An Artificial Intelligence Based Data-driven Approach for Design Ideation
    Liuqing Chen, Pan Wang, Hao Dong, Feng Shi, Ji Han, Yike Guo, Peter RN Childs, Jun Xiao, Chao Wu
    Journal of Visual Communication and Image Representation 2019.
    SIMGAN: Photo-Realistic Semantic Image Manipulation Using Generative Adversarial Networks
    Simiao Yu, Hao Dong, Felix Liang, Yuanhan Mo, Chao Wu, Yike Guo
    Int. Conf. on Image Processing (ICIP) 2019 (Oral).
    Conditional Image Synthesis Using Stacked Auxiliary Classifier Generative Adversarial Networks
    Zhongwei Yao, Hao Dong, Pan Wang, Chao Wu, Yike Guo
    Future of Information and Communications Conference (FICC) 2018.
    Generative Creativity: Adversarial Learning for Bionic Design
    Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo
    Neural Inform. Process. Systems (NeurIPS) Workshop 2018.
    Text-to-Image Synthesis via Visual-Memory Creative Adversarial Network
    Shengyu Zhang, Hao Dong, Wei Hu, Yike Guo, Chao Wu, Di Xie, Fei Wu
    Pacific Rim Conference on Multimedia (PCM) 2018.
    Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security
    Hao Dong, Chao Wu, Wei Zhen, Yike Guo
    IEEE Trans. on Inform. Forensics and Security (TIFS) 2018.
    Towards Desynchronisation Detection in Biosignals
    Akara Supratak, Steffen Schneider, Hao Dong, Ling Li, Yike Guo
    Neural Inform. Process. Systems (NeurIPS) Time Series Workshop 2017.
    SisGAN: Semantic Image Synthesis via Adversarial Learning
    ---Image Manipulation with Natural Language
    Hao Dong*, Simiao Yu*, Chao Wu, Yike Guo
    Int. Conf. on Computer Vision (ICCV) 2017.
    TensorLayer: A Versatile Library for Efficient Deep Learning Development
    Hao Dong, Akara Supratak, Luo Mai, Fangde Liu, Axel Oehmichen, Simiao Yu, Yike Guo
    ACM Multimedia (MM) 2017 (Winner of the Best Open Source Software Award).
    DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
    Guang Yang*, Simiao Yu*, Hao Dong, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, Yike Guo, David Firmin
    IEEE Trans. Med. Imag. (TMI) 2017.
    Deep De-Aliasing for Fast Compressive Sensing MRI
    Simiao Yu*, Hao Dong*, Guang Yang, Greg Slabaugh, Pier Luigi Dragotti, Xujiong Ye, Fangde Liu, Simon Arridge, Jennifer Keegan, David Firmin, Yike Guo
    arXiv:1705.07137 2017.
    I2T2I: Learning Text to Image Synthesis with Textual Data Augmentation
    Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo
    Int. Conf. on Image Processing (ICIP) 2017 (Oral).
    Unsupervised Image-to-Image Translation with Generative Adversarial Networks
    Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo
    arXiv:1701.02676 2017.
    DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
    Akara Supratak, Hao Dong, Chao Wu, Yike Guo
    IEEE Trans. on Neural Systems and Rehabilitation Eng. (TNSRE) 2017.
    Mixed Neural Network Approach for Temporal Sleep Stage Classification
    Hao Dong, Akara Supratak, Wei Pan, Chao Wu, Paul M Matthews, Yike Guo
    IEEE Trans. on Neural Systems and Rehabilitation Eng. (TNSRE) 2017.
    Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
    Hao Dong, Guang Yang, Fangde Liu, Yuanhan Mo, Yike Guo
    Medical Image Understanding and Analysis (MIUA) 2017 (Oral).
    TensorDB: Database Infrastructure for Continuous Machine Learning
    Fangde Liu, Axel Oehmichen, Jingqing Zhang, Kai Sun, Hao Dong, Yuanman Mo, Yike Guo
    Int. Conf. Artificial Intelligence (ICAI) 2017.
    A New Soft Material based In-the-Ear EEG Recording Technique
    Hao Dong, Paul M Matthews, Yike Guo
    Int. Eng. in Medicine and Biology Conf. (EMBC) 2016 (Oral).
    DropNeuron: Simplifying the Structure of Deep Neural Networks
    Wei Pan, Hao Dong, Yike Guo
    arXiv:1606.07326 2016.