Hao Dong 董豪 助理教授 博士生导师 北京大学-信息科学技术学院-前沿计算研究中心 hao.dong@pku.edu.cn Google Scholar / Github: Person / Lab

About Me

I am an assistant professor at Peking University studying AI, vision and machine learning.

Previously, I obtained my Ph.D. degree from Imperial College London under the supervision of Yike Guo in fall 2019. 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 brain-computer interface with Yike Guo between 2012 and 2014.

Hyperplane Lab

We study how to improve the learning ability of artificial intelligence systems. We are especially interested in computer vision and reinforcement learning with the goal of recreating and interacting the world.

Topics we currently focus on include 3D vision, generative models and robotics. We have broader interests in medical data analysis and computer graphics. We also enjoy developing and maintaining open source projects, e.g., TensorLayer, a deep learning and reinforcement learning library for scientists and engineers, which won the Best Open Source Software Award at ACM Multimedia 2017. We are now developing a new project for AI, please contact us for the detail.

The lab is part of the CFCS, Computer Science Department at PKU.

PhD Students Ruihai Wu Mingdong Wu ... (join us)
	Postdocs Lin Dong ... (join us)

Courses

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 CVPR(21), 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)

Programme Commitee of IEEE CBMS

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. 深度强化学习：基础、研究与应用 董豪 丁子涵 仉尚航 等著（中文译本） 电子工业出版社 2021 ISBN: Coming Soon. [Homepage] [Springer] [免费中文在线] [京东]
	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（电子工业出版社）2018 ISBN: 9787121326226. [Amazon] [京东] [Broadview] [Code] [Organisation] [Documentation]
	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. [Springer]
Recent Papers
	End-to-End Object Detection with Adaptive Clustering Transformer Minghang Zheng, Peng Gao, Xiaogang Wang, Hongsheng Li, Hao Dong arXiv 2011.09315. [Paper] [Code]
	P4Contrast: Contrastive Learning with Pairs of Point-Pixel Pairs for RGB-D Scene Understanding Yunze Liu, Li Yi, Shanghang Zhang, Qingnan Fan, Thomas Funkhouser, Hao Dong arXiv 2012.13089. [Paper] [Code]
	Generative 3D Part Assembly via Dynamic Graph Learning Jialei Huang*, Guanqi Zhan*, Qingnan Fan, Kaichun Mo, Lin Shao, Baoquan Chen, Leonidas Guibas, Hao Dong NeurIPS 2020. [Paper] [Code] [Project]
	ACL-GAN: Unpaired Image-to-Image Translation using Adversarial Consistency Loss Yihao Zhao, Ruihai Wu, Hao Dong European Conference on Computer Vision (ECCV) 2020. [Paper] [Code]
	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). [Paper]
	Bilateral Asymmetry Guided Counterfactual Generating Network for Mammogram Classification Chu-ran Wang*, Jing Li*, Fandong Zhang, Xinwei Sun􏰀, Hao Dong, Yizhou Yu, and Yizhou Wang arXiv:2009.14406 2020. [Paper]
	RLzoo: A Comprehensive and Adaptive Reinforcement Learning Library Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Luo Mai, Hao Dong arXiv:2009.08644 2020. [Paper] [Code]
	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. [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. [Paper]
	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. [Paper]
	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). [Paper]
	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. [Paper]
	Generative Creativity: Adversarial Learning for Bionic Design Simiao Yu, Hao Dong, Pan Wang, Chao Wu, Yike Guo Neural Inform. Process. Systems (NeurIPS) Workshop 2018. [Paper]
	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. [Paper]
	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. [Paper]
	Towards Desynchronisation Detection in Biosignals Akara Supratak, Steffen Schneider, Hao Dong, Ling Li, Yike Guo Neural Inform. Process. Systems (NeurIPS) Time Series Workshop 2017. [Paper] [Project]
	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. [Paper]
	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). [Paper] [Code] [Organisation] [Documentation]
	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. [Paper] [Code]
	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. [Paper]
	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). [Paper] [Code]
	Unsupervised Image-to-Image Translation with Generative Adversarial Networks Hao Dong, Paarth Neekhara, Chao Wu, Yike Guo arXiv:1701.02676 2017. [Paper] [Code]
	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. [Paper] [Code]
	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. [Paper]
	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). [Paper]
	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. [Paper]
	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). [Paper]
	DropNeuron: Simplifying the Structure of Deep Neural Networks Wei Pan, Hao Dong, Yike Guo arXiv:1606.07326 2016. [Paper] [Code]

Activities

[12/2020] Panel discussion at WAVE Summit

[11/2020] 我们的《深度强化学习：基础、研究与应用 》中文书将在2021年春节出版，敬请关注。

[11/2020]「Talk」百度 《开源开放平台建设》

[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!

[07/2020]「Talk」中科院文献情报中心 《信息科学技术的开源开放与知识传播》

[06/2020]「Talk」人工智能大会 - 开发者日 机器之心 WAIC《人工智能与开源开放》

[06/2020]「Talk」Tsinghua University - From Deep Generation to Creation

[08/2019] I graduated from Imperial and joined PKU.

[06/2019] Release RL Model Zoo for teaching and research.

[05/2019] Release TensorLayer 2.0 ! A BIG Updated!

[05/2019]「Talk」GAMES 2019, Introduction of Generative Adversarial Networks.

[04/2019]「Talk」Invited talk, "Deep Learning & Data Efficiency" by CFCS, Peking University.

[12/2018]「Talk」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

[05/2016] Deputy leader of the machine learning group in Data Science Institute at Imperial College

[04/2016]「Talk」Invited talk, “Introduction of Artificial Neural Network” at Imperial College

[09/2016] TensorLayer is released. It has quickly gained over 2000+ stars on Github!