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ORCIDTian Han 0001
Person information
- affiliation: Stevens Institute of Technology, Hoboken, NJ, USA
- affiliation (PhD): UCLA, CA, USA
- affiliation (former): HKUST, Hong Kong, SAR, China
- affiliation (former): Hefei University of Technology, China
Other persons with the same name
- Tian Han — disambiguation page
- Tian Han 0002 — Wuhan University of Technology, Wuhan, China
- Tian Han 0003 — BC Geological Survey Branch, Government of BC, Victoria, Canada (and 1 more)
- Tian Han 0004 — University of Science and Technology Beijing, Beijing, China
- Tian Han 0005 — Pukyong National University, Busan, South Korea
- Tian Han 0007
![[0000-0002-4385-6082]](https://dblp1.uni-trier.de/img/orcid-mark.12x12.png "0000-0002-4385-6082")
— Harbin University of Science and Technology, Harbin, China - Tian Han 0008 — Xi'an Jiaotong University, Systems Engineering Institute, China
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2020 – today
- 2023
[c23]Jiali Cui, Ying Nian Wu, Tian Han:
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. CVPR 2023: 3603-3612- [c22]Jiali Cui, Ying Nian Wu, Tian Han:
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. ICCV 2023: 2218-2227 - [c21]Jiali Cui, Tian Han:
Learning Energy-based Model via Dual-MCMC Teaching. NeurIPS 2023 - [c20]Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu:
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. UAI 2023: 1109-1120 - [i23]Jiali Cui, Ying Nian Wu, Tian Han:
Learning Joint Latent Space EBM Prior Model for Multi-layer Generator. CoRR abs/2306.06323 (2023) - [i22]Deqian Kong, Bo Pang, Tian Han, Ying Nian Wu:
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting. CoRR abs/2306.14902 (2023) - [i21]Jiali Cui, Ying Nian Wu, Tian Han:
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior. CoRR abs/2310.09604 (2023) - [i20]Jiali Cui, Tian Han:
Learning Energy-based Model via Dual-MCMC Teaching. CoRR abs/2312.02469 (2023) - 2022
- [j5]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): 1162-1179 (2022) - [c19]Yizhou Zhao, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu:
Learning from the Tangram to Solve Mini Visual Tasks. AAAI 2022: 3490-3498 - [c18]Chang Lu, Tian Han, Yue Ning:
Context-Aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs. AAAI 2022: 4567-4574 - [c17]Zhisheng Xiao, Tian Han:
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model. NeurIPS 2022 - [i19]Zhisheng Xiao, Tian Han:
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model. CoRR abs/2209.08739 (2022) - 2021
- [j4]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: 106675 (2021) - [c16]Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu:
Generative Text Modeling through Short Run Inference. EACL 2021: 1156-1165 - [c15]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: 1-6 - [i18]Bo Pang, Erik Nijkamp, Tian Han, Ying Nian Wu:
Generative Text Modeling through Short Run Inference. CoRR abs/2106.02513 (2021) - [i17]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 abs/2107.07089 (2021) - [i16]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 abs/2107.08821 (2021) - [i15]Chang Lu, Tian Han, Yue Ning:
Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs. CoRR abs/2112.05195 (2021) - [i14]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 abs/2112.06060 (2021) - [i13]Yizhou Zhao, Liang Qiu, Pan Lu, Feng Shi, Tian Han, Song-Chun Zhu:
Learning from the Tangram to Solve Mini Visual Tasks. CoRR abs/2112.06113 (2021) - 2020
- [c14]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: 5272-5280 - [c13]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: 7975-7984 - [c12]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: 361-378 - [c11]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: 1-6 - [c10]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: 1665-1671 - [c9]Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model. NeurIPS 2020 - [i12]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 abs/2006.06059 (2020) - [i11]Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model. CoRR abs/2006.08205 (2020) - [i10]Bo Pang, Tian Han, Ying Nian Wu:
Learning Latent Space Energy-Based Prior Model for Molecule Generation. CoRR abs/2010.09351 (2020) - [i9]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 abs/2010.09359 (2020)
2010 – 2019
- 2019
- [b1]Tian Han:
Unsupervised Learning and Understanding of Deep Generative Models. University of California, Los Angeles, USA, 2019 - [j3]Dandan Zhu, Qiangqiang Zhou, Tian Han, Yongqing Chen:
360 Degree Panorama Synthesis From Sequential Views Based on Improved FC-Densenets. IEEE Access 7: 180503-180511 (2019) - [j2]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): 2348-2367 (2019) - [c8]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: 8670-8679 - [c7]Xianglei Xing, Tian Han, Ruiqi Gao, Song-Chun Zhu, Ying Nian Wu:
Unsupervised Disentangling of Appearance and Geometry by Deformable Generator Network. CVPR 2019: 10354-10363 - [c6]Tian Han, Yang Lu, Jiawen Wu, Xianglei Xing, Ying Nian Wu:
Learning Generator Networks for Dynamic Patterns. WACV 2019: 809-818 - [i8]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 abs/1903.12370 (2019) - [i7]Dandan Zhu, Tian Han, Linqi Zhou, Xiaokang Yang, Ying Nian Wu:
Deep Unsupervised Clustering with Clustered Generator Model. CoRR abs/1911.08459 (2019) - [i6]Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, Ying Nian Wu:
Learning Deep Generative Models with Short Run Inference Dynamics. CoRR abs/1912.01909 (2019) - 2018
- [j1]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): 219-227 (2018) - [c5]Tian Han, Xianglei Xing, Ying Nian Wu:
Learning Multi-view Generator Network for Shared Representation. ICPR 2018: 2062-2068 - [c4]Tian Han, Jiawen Wu, Ying Nian Wu:
Replicating Active Appearance Model by Generator Network. IJCAI 2018: 2205-2211 - [i5]Tian Han, Jiawen Wu, Ying Nian Wu:
Replicating Active Appearance Model by Generator Network. CoRR abs/1805.08704 (2018) - [i4]Xianglei Xing, Ruiqi Gao, Tian Han, Song-Chun Zhu, Ying Nian Wu:
Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry. CoRR abs/1806.06298 (2018) - [i3]Ying Nian Wu, Ruiqi Gao, Tian Han, Song-Chun Zhu:
A Tale of Three Probabilistic Families: Discriminative, Descriptive and Generative Models. CoRR abs/1810.04261 (2018) - [i2]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 abs/1812.10907 (2018) - 2017
- [c3]Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu:
Alternating Back-Propagation for Generator Network. AAAI 2017: 1976-1984 - 2016
- [i1]Tian Han, Yang Lu, Song-Chun Zhu, Ying Nian Wu:
Learning Generative ConvNet with Continuous Latent Factors by Alternating Back-Propagation. CoRR abs/1606.08571 (2016) - 2012
- [c2]Tian Han, Chun Liu, Chiew-Lan Tai, Long Quan:
Quasi-regular Facade Structure Extraction. ACCV (4) 2012: 552-564 - [c1]Chao Yang, Tian Han, Long Quan, Chiew-Lan Tai:
Parsing façade with rank-one approximation. CVPR 2012: 1720-1727
Coauthor Index
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