Tutorials:
VALSE 2022 Keynote
[Website]
CVPR 2022 Workshop on the Art of Robustness
[Website]
VALSE 2021 Tutorial on Interpretable Machine Learning
[Website]
世界人工智能大会（WAIC）可信AI论坛 Panel Discussion
[Website]
IJCAI 2021 Tutorial on Theoretically Unifying Conceptual Explanation and Generalization of DNNs
[Website] [Video]
IJCAI 2020 Tutorial on Trustworthiness of Interpretable Machine Learning
[Website] [Video]
ICML 2020 Online Panel Discussion: “Baidu AutoDL: Automated and Interpretable Deep Learning”
PRCV 2020 Tutorial on Robust and Explainable Artificial Intelligence
[Website]
Publications: [ Network Interpretability ] [ Graph Mining & Matching ] [ Other Studies ]
Where We Have Arrived in Proving the Emergence of Sparse Interaction Primitives in AI Models
Qihan Ren, Jiayang Gao, Wen Shen, Quanshi Zhang (Correspondence)
ICLR, 2024
[PDF]
Defining and Extracting generalizable interaction primitives from DNNs
Lu Chen*, Siyu Lou*, Benhao Huang, and Quanshi Zhang (Correspondence)
ICLR, 2024
[PDF]
Explaining Generalization Power of a DNN using Interactive Concepts
Huilin Zhou, Hao Zhang, Huiqi Deng, Dongrui Liu, Wen Shen, Shih-Han Chan, and Quanshi Zhang (Correspondence)
AAAI, 2024
[PDF]
Batch Normalization Is Blind to the First and Second Derivatives of the Loss
Zhanpeng Zhou*, Wen Shen*, Huixin Chen*, Ling Tang, Yuefeng Chen, and Quanshi Zhang (Correspondence)
AAAI, 2024
[PDF]
Clarifying the Behavior and the Difficulty of Adversarial Training
Xu Cheng, Hao Zhang, Yue Xin, Wen Shen, Jie Ren, and Quanshi Zhang (Correspondence)
AAAI, 2024
[PDF]
Towards the Difficulty for a Deep Neural Network to Learn Concepts of Different Complexities
Dongrui Liu*, Huiqi Deng*, Xu Cheng, Qihan Ren, Kangrui Wang, and Quanshi Zhang (Correspondence)
NeurIPS, 2023
[PDF]
Does a Neural Network Really Encode Symbolic Concept?
Mingjie Li, and Quanshi Zhang (Correspondence)
ICML, 2023
[PDF]
Bayesian Neural Networks Avoid Encoding Perturbation-sensitive and Complex Concepts
Qihan Ren*, Huiqi Deng*, Yunuo Chen, Siyu Lou, and Quanshi Zhang (Correspondence)
ICML, 2023
[PDF]
Defining and Quantifying the Emergence of Sparse Concepts in DNNs
Jie Ren*, Mingjie Li*, Qirui Chen, Huiqi Deng, and Quanshi Zhang (Correspondence)
CVPR 2023
[PDF]
Exploring Image Regions Not Well Encoded by an INN
Zenan Ling, Fan Zhou, Meng Wei, and Quanshi Zhang (Correspondence)
AISTATS, 2022
[PDF]
Interpretable Generative Adversarial Networks
Chao Li (Correspondence), Kelu Yao, Jin Wang, Boyu Diao, Yongjun Xu, and Quanshi Zhang (Correspondence)
AAAI (Oral), 2022
[PDF]
Visualizing the Emergence of Intermediate Visual Patterns in DNNs
Mingjie Li, Shaobo Wang, and Quanshi Zhang (Correspondence)
Neurips, 2021
[PDF]
Explaining Knowledge Distillation by Quantifying the Knowledge
Xu Cheng, Zhefan Rao, Yilan Chen, and Quanshi Zhang (Correspondence)
CVPR 2020
[PDF]
Interpretable Complex-Valued Neural Networks for Privacy Protection
Liyao Xiang, Hao Zhang, Haotian Ma, Yifan Zhang, Jie Ren, and Quanshi Zhang (Correspondence)
ICLR 2020
[arXiv]
Explaining Neural Networks Semantically and Quantitatively
Runjin Chen, Hao Chen, Jie Ren, Ge Huang, Quanshi Zhang (Correspondence)
ICCV, 2019 (Oral)
[PDF]
Unsupervised Learning of Neural Networks to Explain Neural Networks
Quanshi Zhang, Yu Yang, Yuchen Liu, Ying Nian Wu, and Song-Chun Zhu
Full paper in arXiv:1805.07468, 2018
Extended abstract in AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019
[PDF]
Network Transplanting
Quanshi Zhang, Yu Yang, Qian Yu, Ying Nian Wu, and Song-Chun Zhu
Full paper in arXiv:1804.10272, 2018
Extended abstract in AAAI-19 Workshop on Network Interpretability for Deep Learning, 2019
[PDF]
Interpretable Convolutional Neural Networks
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu
CVPR (Spotlight) 2018
[PDF] [Code] [Project website]
Interpreting CNN Knowledge via an Explanatory Graph
Quanshi Zhang, Ruiming Cao, Feng Shi, Ying Nian Wu, and Song-Chun Zhu
AAAI, 2018
[PDF] [Code] [Project website] [Video]
Examining CNN Representations with respect to Dataset Bias
Quanshi Zhang, Wenguan Wang, and Song-Chun Zhu
AAAI, 2018
[PDF]
Mining Object Parts from CNNs via Active Question-Answering
Quanshi Zhang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu
CVPR, 2017
[Project website] [PDF] [Video]
Growing Interpretable Part Graphs on ConvNets via Multi-Shot Learning
Quanshi Zhang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu
AAAI, 2017
[PDF] [Code] [Project website] [Video]
Unifying Fourteen Post-Hoc Attribution Methods With Taylor Interactions
Huiqi Deng, Na Zou, Mengnan Du, Weifu Chen, Guocan Feng, Ziwei Yang, Zheyang Li, Quanshi Zhang (Correspondence)
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2024
[PDF]
Interpretable Rotation-Equivariant Quaternion Neural Networks for 3D Point Cloud Processing
Wen Shen∗, Zhihua Wei∗, Qihan Ren, Binbin Zhang, Shikun Huang, Jiaqi Fan, and Quanshi Zhang (Correspondence)
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2024
[PDF]
Interpretability of Neural Networks Based on Game-Theoretic Interactions
Huilin Zhou, Jie Ren, Huiqi Deng, Xu Cheng, Jinpeng Zhang, and Quanshi Zhang (Correspondence)
Machine Intelligence Research, 2023
[PDF]
Quantifying the Knowledge in a DNN to Explain Knowledge Distillation for Classification
Quanshi Zhang* (Correspondence), Xu Cheng*, Yilan Chen, and Zhefan Rao
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2022
[PDF]
Interpretable CNNs for Object Classification
Quanshi Zhang, Xin Wang, Ying Nian Wu, Huilin Zhou, and Song-Chun Zhu
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2020 (online available)
[PDF] [Code] [Project website]
Extraction of an Explanatory Graph to Interpret a CNN
Quanshi Zhang, Xin Wang, Ruiming Cao, Ying Nian Wu, Feng Shi, and Song-Chun Zhu
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2020 (online available)
[PDF] [Code] [Project website] [Video]
Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering
Quanshi Zhang, Jie Ren, Ge Huang, Ruiming Cao, Ying Nian Wu, and Song-Chun Zhu
IEEE Transactions on Pattern Analysis and Machine Intelligence (IEEE T-PAMI), 2020 (online available)
[PDF]
Visual interpretability for Deep Learning: a Survey
Quanshi Zhang and Song-Chun Zhu
Frontiers of Information Technology & Electronic Engineering. Vol. 19, No. 1, page 27-39, 2018 (best paper)
[PDF] [Project website] [机器之心]
Proving Common Mechanisms Shared by Twelve Methods of Boosting Adversarial Transferability
Quanshi Zhang* (Correspondence), Xin Wang*, Jie Ren*, Xu Cheng, Shuyun Lin, Yisen Wang, Xiangming Zhu
arXiv:2207.11694, 2022
[PDF]
Trap of Feature Diversity in the Learning of MLPs
Dongrui Liu, Shaobo Wang, Jie Ren, Kangrui Wang, Sheng Yin, and Quanshi Zhang (Correspondence)
arXiv:2112.00980, 2021
[PDF]
Rapid Detection and Recognition of Whole Brain Activity in a Freely Behaving Caenorhabditis Elegans
Yuxiang Wu, Shang Wu, Xin Wang, Chengtian Lang, Quanshi Zhang, Quan Wen, and Tianqi Xu
arXiv:2109.10474, 2021
[PDF]
A Hypothesis for the Aesthetic Appreciation in Neural Networks
Xu Cheng, Xin Wang, Haotian Xue, Zhengyang Liang, and Quanshi Zhang (Correspondence)
arXiv:2108.02646, 2021
[PDF]
A Game-Theoretic Taxonomy of Visual Concepts in DNNs
Xu Cheng, Chuntung Chu, Yi Zheng, Jie Ren, and Quanshi Zhang (Correspondence)
arXiv:2106.10938, 2021
[PDF]
Rotation-Equivariant Neural Networks for Privacy Protection
Hao Zhang, Yiting Chen, Haotian Ma, Xu Cheng, Qihan Ren, Liyao Xiang, Jie Shi, and Quanshi Zhang
arXiv:2006.13016, 2020
[PDF]
Deep Quaternion Features for Privacy Protection
Hao Zhang, Yiting Chen, Liyao Xiang, Haotian Ma, Jie Shi, and Quanshi Zhang
arXiv:2003.08365, 2020
[PDF]
Towards a Unified Evaluation of Explanation Methods without Ground Truth
Hao Zhang, Jiayi Chen, Haotian Xue, and Quanshi Zhang
arXiv:1911.09017, 2019
[PDF]
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Zenan Ling, Haotian Ma, Yu Yang, Robert C. Qiu1, Song-Chun Zhu, and Quanshi Zhang
arXiv:1901.02184, 2019
[PDF]
Interactively Transferring CNN Patterns for Part Localization
Quanshi Zhang, Ruiming Cao, Shengming Zhang, Ying Nian Wu, and Song-Chun Zhu
arXiv:1708.01783, 2017
[PDF]
Visual Graph Mining for Graph Matching
Quanshi Zhang, Xuan Song, Yu Yang, Haotian Ma, Ryosuke Shibasaki
Computer Vision and Image Understanding, vol. 178, page 16-29, 2019
[PDF]
Mining Deep And-Or Object Structures via Cost-Sensitive Question-Answer-Based Active Annotations
Quanshi Zhang, Ying Nian Wu, Hao Zhang, and Song-Chun Zhu
Computer Vision and Image Understanding, vol. 176-177, page 33-44, 2018
[PDF]
Object Discovery: Soft Attributed Graph Mining
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 38(3):532-545, 2016
[PDF] [Project website] [Code]
From RGB-D Images to RGB Images: Single Labeling for Mining Visual Models
Quanshi Zhang, Xuan Song, Xiaowei Shao, Ryosuke Shibasaki, Huijing Zhao
ACM Transactions on Intelligent Systems and Technology (ACM-TIST), 6(2): 16, 2015
[PDF]
Conference papers
Mining And-Or Graphs for Graph Matching and Object Discovery
Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu
ICCV, 2015
[PDF] [Video] [Supplementary] [Project website] [Code]
Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
CVPR, 2014
[PDF] [Video] [Project website] [Video Spotlight] [Code]
Learning Graph Matching: Oriented to Category Modeling from Cluttered Scenes
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
ICCV 2013
[PDF]
Journal papers
Rapid Detection and Recognition of Whole Brain Activity in a Freely Behaving Caenorhabditis Elegans
Yuxiang Wu, Shang Wu, Xin Wang, Chengtian Lang, Quanshi Zhang, Quan Wen, and Tianqi Xu
PLOS Computational Biology, 2022
[PDF]
Prediction and Simulation of Human Mobility Following Natural Disasters
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, and Xing Xie
ACM Transactions on Intelligent Systems and Technology (ACM-TIST), 8(2): 29, 2016
[PDF]
Intelligent System for Human Behavior Analysis and Reasoning Following Large‐scale Disasters
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, and Ryosuke Shibasaki
IEEE Intelligent Systems, vol. 28, no. 4, pp. 35-42, July-Aug. 2013
[PDF]
A Fully Online and Unsupervised System for Large and High Density Area Surveillance: Tracking, Semantic Scene Learning and Abnormality Detection
Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, Jinshi Cui, and Hongbin Zha
ACM Transactions on Intelligent Systems and Technology (ACM‐TIST), 4(2): 20, 2013
[PDF]
A Novel Dynamic Model for Multiple Pedestrians Tracking in Extremely Crowded Scenarios
Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, and Hongbin Zha
Information Fusion, Elsevier, 2012
[PDF]
arXiv papers
Conference papers
Learning to Prevent Input Leakages in the Mobile Cloud Inference
Liyao Xiang, Shuang Zhang, and Quanshi Zhang
IEEE Transactions on Mobile Computing, 2023
[PDF]
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL
Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang, and Zhouhan Lin
EMNLP, 2022
[PDF]
A Simulator of Human Emergency Mobility following Disasters: Knowledge Transfer from Big Disaster Data
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Ryosuke Shibasaki, Nicholas Jing Yuan, and Xing Xie
AAAI, 2015
[PDF]
When 3D Reconstruction Meets Ubiquitous RGB-D Images
Quanshi Zhang, Xuan Song, Xiaowei Shao, Huijing Zhao, and Ryosuke Shibasaki
CVPR, 2014
[PDF] [Video] [Video spotlight]
Prediction of Human Emergency Behavior and their Mobility following Large-scale Disaster
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki
KDD, 2014
[PDF]
Intelligent System for Urban Emergency Management During Large-scale Disaster
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, and Ryosuke Shibasaki
AAAI, 2014
[PDF]
Modeling and Probabilistic Reasoning of Population Evacuation During Large-scale Disaster
Xuan Song, Quanshi Zhang, Yoshihide Sekimoto, Teerayut Horanont, Satoshi Ueyama, and Ryosuke Shibasaki
KDD, 2013
[PDF]
Laser-based Intelligent Surveillance and Abnormality Detection in Extremely Crowded Scenarios
Xuan Song, Xiaowei Shao, Quanshi Zhang, Ryosuke Shibasaki, Huijing Zhao, and Hongbin Zha
ICRA, 2012
[PDF]
Moving Object Classification using Horizontal Laser Scan Data
Huijing Zhao, Quanshi Zhang, Masaki Chiba, Ryosuke Shibasaki, Jinshi Cui, and Hongbin Zha
ICRA, 2009
[PDF]