Tian Han

ORCID

Publication Activity (10 Years)

Years Active: 2012-2024
Publications (10 Years): 47

Top Venues

University of California, Los Angeles, USA (PHD)

Education History

University of California, Los Angeles, USA

PHD

Publications

Jiali Cui, Tian Han
Learning Latent Space Hierarchical EBM Diffusion Models. CoRR (2024)
Jiali Cui, Ying Nian Wu, Tian Han
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. CVPR (2023)
Jiali Cui, Ying Nian Wu, Tian Han
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. ICCV (2023)
Jiali Cui, Ying Nian Wu, Tian Han
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. CoRR (2023)
Jiali Cui, Tian Han
Learning Energy-based Model via Dual-MCMC Teaching. CoRR (2023)
Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. CoRR (2023)
Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. UAI (2023)
Jiali Cui, Tian Han
Learning Energy-based Model via Dual-MCMC Teaching. NeurIPS (2023)
Jiali Cui, Ying Nian Wu, Tian Han
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. CoRR (2023)
Zhisheng Xiao, Tian Han
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model. NeurIPS (2022)
Yizhou Zhao, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu
Learning from the Tangram to Solve Mini Visual Tasks. AAAI (2022)
Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu
Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry. IEEE Trans. Pattern Anal. Mach. Intell. 44 (3) (2022)
Quanshi Zhang, Tian Han, Lixin Fan, Zhanxing Zhu, Hang Su, Ying Nian Wu, Jie Ren, Hao Zhang
Proceedings of ICML 2021 Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI. CoRR (2021)
Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu
Generative Text Modeling through Short Run Inference. CoRR (2021)
Dandan Zhu, Qiangqiang Zhou, Tian Han, Yongqing Chen, Defang Zhao, Xiaokang Yang
Towards multi-scale deep features learning with correlation metric for person re-identification. Knowl. Based Syst. 213 (2021)
Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu
Generative Text Modeling through Short Run Inference. EACL (2021)
Feng Shi, Chonghan Lee, Liang Qiu, Yizhou Zhao, Tianyi Shen, Shivran Muralidhar, Tian Han, Song-Chun Zhu, Vijaykrishnan Narayanan
STAR: Sparse Transformer-based Action Recognition. CoRR (2021)
Yizhou Zhao, Wensi Ai, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu
GenMotion: Data-driven Motion Generators for Real-time Animation Synthesis. CoRR (2021)
Dandan Zhu, Defang Zhao, Xiongkuo Min, Tian Han, Qiangqiang Zhou, Shaobo Yu, Yongqing Chen, Guangtao Zhai, Xiaokang Yang
Lavs: A Lightweight Audio-Visual Saliency Prediction Model. ICME (2021)
Yizhou Zhao, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu
Learning from the Tangram to Solve Mini Visual Tasks. CoRR (2021)
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu
Learning Latent Space Energy-Based Prior Model. CoRR (2020)
Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. CoRR (2020)
Dandan Zhu, Yongqing Chen, Tian Han, Defang Zhao, Yucheng Zhu, Qiangqiang Zhou, Guangtao Zhai, Xiaokang Yang
Ransp: Ranking Attention Network For Saliency Prediction On Omnidirectional Images. ICME (2020)
Dandan Zhu, Yongqing Chen, Xiongkuo Min, Defang Zhao, Yucheng Zhu, Qiangqiang Zhou, Xiaokang Yang, Tian Han
Saliency Prediction on Omnidirectional Images with Brain-Like Shallow Neural Network. ICPR (2020)
Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
Joint Training of Variational Auto-Encoder and Latent Energy-Based Model. CVPR (2020)
Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu
Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference. ECCV (6) (2020)
Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, Ying Nian Wu
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling. CoRR (2020)
Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu
Learning Latent Space Energy-Based Prior Model. NeurIPS (2020)
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu
On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models. AAAI (2020)
Bo Pang, Tian Han, Ying Nian Wu
Learning Latent Space Energy-Based Prior Model for Molecule Generation. CoRR (2020)
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu
On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models. CoRR (2019)
Dandan Zhu, Tian Han, Linqi Zhou, Xiaokang Yang, Ying Nian Wu
Deep Unsupervised Clustering with Clustered Generator Model. CoRR (2019)
Dandan Zhu, Qiangqiang Zhou, Tian Han, Yongqing Chen
360 Degree Panorama Synthesis From Sequential Views Based on Improved FC-Densenets. IEEE Access 7 (2019)
Xianglei Xing, Tian Han, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. CVPR (2019)
Tian Han, Xianglei Xing, Jiawen Wu, Ying Nian Wu
Replicating Neuroscience Observations on ML/MF and AM Face Patches by Deep Generative Model. Neural Comput. 31 (12) (2019)
Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inferential Model. CVPR (2019)
Tian Han, Yang Lu, Jiawen Wu, Xianglei Xing, Ying Nian Wu
Learning Generator Networks for Dynamic Patterns. WACV (2019)
Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu
Learning Deep Generative Models with Short Run Inference Dynamics. CoRR (2019)
Tian Han, Xianglei Xing, Ying Nian Wu
Learning Multi-view Generator Network for Shared Representation. ICPR (2018)
Tian Han, Jiawen Wu, Ying Nian Wu
Replicating Active Appearance Model by Generator Network. IJCAI (2018)
Tian Han, Jiawen Wu, Ying Nian Wu
Replicating Active Appearance Model by Generator Network. CoRR (2018)
Ying Nian Wu, Ruiqi Gao, Tian Han, Song-Chun Zhu
A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models. CoRR (2018)
Kejun Wang, Haolin Wang, Meichen Liu, Xianglei Xing, Tian Han
Survey on person re-identification based on deep learning. CAAI Trans. Intell. Technol. 3 (4) (2018)
Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Model. CoRR (2018)
Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu
Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry. CoRR (2018)
Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu
Alternating Back-Propagation for Generator Network. AAAI (2017)
Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu
Learning Generative ConvNet with Continuous Latent Factors by Alternating Back-Propagation. CoRR (2016)
Tian Han, Chun Liu, Chiew-Lan Tai, Long Quan
Quasi-regular Facade Structure Extraction. ACCV (4) (2012)
Chao Yang, Tian Han, Long Quan, Chiew-Lan Tai
Parsing façade with rank-one approximation. CVPR (2012)

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Publication Activity (10 Years)

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Tian Han