Papers of Energy-Based Models

(Filters, Random Fields and Maximum Entropy) FRAME Models

1.

Filters, Random Field, And Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling

Song-Chun Zhu, Ying Nian Wu, David Mumford

International Journal of Computer Vision (IJCV) 1997

2.

Grade: Gibbs Reaction and Diffusion Equations

Song-Chun Zhu, David Mumford

International Conference on Computer Vision (ICCV) 1998

3.

Equivalence of Julesz Ensembles and FRAME Models

Ying Nian Wu, Song-Chun Zhu, Xiuwen Liu

International Journal of Computer Vision (IJCV) 2000

4.

Learning Inhomogeneous FRAME Models for Object Patterns

Jianwen Xie, Wenze Hu, Song-Chun Zhu, Ying Nian Wu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014

5.

Learning Sparse FRAME Models for Natural Image Patterns

Jianwen Xie, Wenze Hu, Song-Chun Zhu, and Ying Nian Wu

International Journal of Computer Vision (IJCV) 2014.

6.

Inducing Wavelets into Random Fields via Generative Boosting

Jianwen Xie, Yang Lu, Song-Chun Zhu, and Ying Nian Wu

Journal of Applied and Computational Harmonic Analysis (ACHA) 2015.

7.

Generative Hierarchical Learning of Sparse FRAME Models

Jianwen Xie, Yifei Xu, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017

8.

Learning FRAME Models Using CNN Filters

Yang Lu, Song-Chun Zhu, and Ying Nian Wu

AAAI Conference on Artificial Intelligence (AAAI) 2016

9.

Sparse and Deep Generalizations of the FRAME Model

Ying Nian Wu, Jianwen Xie, Yang Lu, Song-Chun Zhu

Annals of Mathematical Sciences and Applications (AMSA) 2018

Energy-Based Generative ConvNet

1.

A Theory of Generative ConvNet

Jianwen Xie *, Yang Lu *, Song-Chun Zhu, and Ying Nian Wu

International Conference on Machine Learning (ICML) 2016 (* equal contributions)

2.

Synthesizing Dynamic Pattern by Spatial-Temporal Generative ConvNet

Jianwen Xie, Song-Chun Zhu, and Ying Nian Wu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017

3.

Learning Energy-Based Spatial-Temporal Generative ConvNet for Dynamic Patterns

Jianwen Xie, Song-Chun Zhu, and Ying Nian Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019

4.

Learning Descriptor Networks for 3D Shape Synthesis and Analysis

Jianwen Xie *, Zilong Zheng *, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018

5.

Generative VoxelNet: Learning Energy-Based Models for 3D Shape Synthesis and Analysis

Jianwen Xie *, Zilong Zheng *, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, Ying Nian Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019

6.

Learning Energy-Based Models as Generative ConvNets via Multi-grid Modeling and Sampling

Ruiqi Gao*, Yang Lu*, Junpei Zhou, Song-Chun Zhu, Ying Nian Wu (* equal contributions)

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018

7.

On Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model

Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu

Neural Information Processing Systems (NeurIPS) 2019

8.

Energy-Based Continuous Inverse Optimal Control

Yifei Xu, Jianwen Xie, Tianyang Zhao, Chris Baker, Yibiao Zhao, Ying Nian Wu

IEEE Transactions on Neural Networks and Learning Systems (TNNLS) 2022

9.

Generative PointNet: Deep Energy-Based Learning on Unordered Point Sets for 3D Generation, Reconstruction and Classification

Jianwen Xie *, Yifei Xu *, Zilong Zheng, Song-Chun Zhu, Ying Nian Wu (* equal contributions)

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021

10.

Patchwise Generative ConvNet: Training Energy-Based Models from a Single Natural Image for Internal Learning

Zilong Zheng, Jianwen Xie, Ping Li

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021

11.

Learning Energy-Based Models by Diffusion Recovery Likelihood

Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P. Kingma

International Conference of Learning Representations (ICLR), 2021

12.

Learning Energy-Based Generative Models via Coarse-to-Fine Expanding and Sampling

Yang Zhao, Jianwen Xie, Ping Li

International Conference on Learning Representations (ICLR) 2021

Energy-Based Generative Cooperative Network

1.

Cooperative Learning of Energy-Based Model and Latent Variable Model via MCMC Teaching

Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, and Ying Nian Wu

AAAI Conference on Artificial Intelligence (AAAI) 2018)

2.

Cooperative Training of Descriptor and Generator Networks

Jianwen Xie, Yang Lu, Ruiqi Gao, Song-Chun Zhu, and Ying Nian Wu

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2019

3.

Cooperative Training of Fast Thinking Initializer and Slow Thinking Solver for Conditional Learning

Jianwen Xie *, Zilong Zheng *, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu (* equal contributions)

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2021

4.

Learning Cycle-Consistent Cooperative Networks via Alternating MCMC Teaching for Unsupervised Cross-Domain Translation

Jianwen Xie *, Zilong Zheng *, Xiaolin Fang, Song-Chun Zhu, Ying Nian Wu (* equal contributions)

AAAI Conference on Artificial Intelligence (AAAI) 2021

5.

Learning Energy-Based Model with Variational Auto-Encoder as Amortized Sampler

Jianwen Xie, Zilong Zheng, Ping Li

AAAI Conference on Artificial Intelligence (AAAI) 2021

6.

Energy-Based Generative Cooperative Saliency Prediction

Jing Zhang, Jianwen Xie, Zilong Zheng, Nick Barnes

AAAI Conference on Artificial Intelligence (AAAI) 2022

7.

A Tale of Two Flows: Cooperative Learning of Langevin Flow and Normalizing Flow Toward Energy-Based Model

Jianwen Xie, Yaxuan Zhu, Jun Li, Ping Li

International Conference on Learning Representations (ICLR) 2022

Latent Space Energy-Based Models

1.

Learning Latent Space Energy-Based Prior Model

Bo Pang*, Tian Han*, Erik Nijkamp*, Song-Chun Zhu, and Ying Nian Wu

Neural Information Processing Systems (NeurIPS) 2020. (*equal contribution)

2.

Learning Latent Space Energy-Based Prior Model for Molecule Generation

Bo Pang, Tian Han, and Ying Nian Wu

Machine Learning for Molecules Workshop at NeurIPS 2020

3.

Semi-Supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling

Bo Pang, Erik Nijkamp, Jiali Cui, Tian Han, and Ying Nian Wu

ICBINB Workshop at NeurIPS 2020

4.

Trajectory Prediction with Latent Belief Energy-Based Model

Bo Pang, Tianyang Zhao, Xu Xie, and Ying Nian Wu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Training EBM without MCMC

1.

Divergence Triangle for Joint Training of Generator Model, Energy-Based Model, and Inference Model

Tian Han*, Erik Nijkamp*, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, Ying Nian Wu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019. (* equal contribution)

2.

Joint Training of Variational Auto-Encoder and Latent Energy-Based Model

Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, and Ying Nian Wu

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020

3.

Flow Contrastive Estimation of Energy-Based Model

Ruiqi Gao, Erik Nijkamp, Diederik Kingma, Zhen Xu, Andrew Dai, and Ying Nian Wu.

IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020

key word: Energy-Based model; generative model; langevin dynamics; random field; mcmc; markov chain monte carlo; likelihood based generative model; maximum likelihood; gan; vae; generative Adversarial network; variational auto-encoder;