Wasserstein Introspective Neural Networks

Deep Residual Learning for Image Recognition

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ImageNet classification with deep convolutional neural networks

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Adam: A Method for Stochastic Optimization

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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Densely Connected Convolutional Networks

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Image-to-Image Translation with Conditional Adversarial Networks

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Generative Adversarial Nets

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Auto-Encoding Variational Bayes

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GAN（Generative Adversarial Nets）

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Layer NormalizationPreprint

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Aggregated Residual Transformations for Deep Neural Networks

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Unsupervised representation learning with deep convolutional generative adversarial networks

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Backpropagation Applied to Handwritten Zip Code Recognition

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Explaining and Harnessing Adversarial Examples

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Deep Learning Face Attributes in the Wild

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Improved Techniques for Training GANs

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Improved training of Wasserstein GANs

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Progressive Growing of GANs for Improved Quality, Stability, and Variation

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Reading digits in natural images with unsupervised feature learning

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A learning algorithm for Boltzmann machines

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A Neural Algorithm of Artistic Style

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Searching for activation functions

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Adversarial machine learning at scale

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Deeply-Supervised NetsPreprint

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Bayesian Learning via Stochastic Gradient Langevin Dynamics

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Adversarially Learned InferencePreprint

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Texture Synthesis Using Convolutional Neural NetworksPreprint

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Information and information stability of random variables and processes

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Texture Networks: Feed-forward Synthesis of Textures and Stylized ImagesPreprint

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Deep Convolutional Inverse Graphics NetworkPreprint

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Learning to generate chairs with convolutional neural networks

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Stacked Generative Adversarial Networks

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Minimax Entropy Principle and Its Application to Texture Modeling

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A Stochastic Grammar of Images

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Information and Information Stability of Random Variables and Processes.

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A Stochastic Grammar of Images

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$f$ -Divergence Inequalities

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A Theory of Generative ConvNetPreprint

Xie, J., Lu, Y., Zhu, S. C., & Wu, Y. N. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.03264 · Cited by 176 · see citing works

Learning Active Basis Model for Object Detection and Recognition

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Improving Generative Adversarial Networks with Denoising Feature Matching

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Learning Deep Energy Models

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Approximation and Convergence Properties of Generative Adversarial LearningPreprint

Liu, S., Bousquet, O., & Chaudhuri, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08991 · Cited by 97 · see citing works

Learning Generative Models via Discriminative Approaches

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

Xie, J., Lu, Y., Gao, R.-Q., & Wu, Y. (2018) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11834 · Cited by 94 · see citing works

Alternating Back-Propagation for Generator Network

Han, T., Lu, Y., Zhu, S. C., & Wu, Y. N. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.10902 · Cited by 87 · see citing works

Introspective Neural Networks for Generative Modeling

Lazarow, J., Jin, L., & Tu, Z. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.302 · Cited by 49 · see citing works

Self Supervised Boosting

Welling, M., Zemel, R. S., & Hinton, G. E. (2002) · Cited by 36 · see citing works

Introspective Classification with Convolutional NetsPreprint

Jin, L., Lazarow, J., & Tu, Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.07816 · Cited by 31 · see citing works

Diffusion Models: A Comprehensive Survey of Methods and Applications

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Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation

Lee, C., Batra, T., Baig, M. H., & Ulbricht, D. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01053 · Cited by 610 · see citing works

A deep translation (GAN) based change detection network for optical and SAR remote sensing images

Li, X., Du, Z., Huang, Y., & Tan, Z. (2021) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2021.07.007 · Cited by 403 · see citing works

Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation

Chen, R., Huang, W., Huang, B., Sun, F., & Fang, B. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00819 · Cited by 259 · see citing works

Learning to Generate Synthetic Data via Compositing

Tripathi, S., Chandra, S., Agrawal, A., Tyagi, A., Rehg, J. M., & Chari, V. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00055 · Cited by 123 · see citing works

Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning

Zhu, Y., Xie, J., Liu, B., & Elgammal, A. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00994 · Cited by 111 · see citing works

Cooperative Training of Descriptor and Generator Networks

Xie, J., Lu, Y., Gao, R., Zhu, S. C., & Wu, Y. N. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2879081 · Cited by 108 · see citing works

Dual Contradistinctive Generative Autoencoder

Parmar, G., Li, D., Lee, K., & Tu, Z. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.00088 · Cited by 70 · see citing works

Deep Set Prediction NetworksPreprint

Zhang, Y., Hare, J. S., & Prügel‐Bennett, A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.06565 · Cited by 71 · see citing works

Flow Contrastive Estimation of Energy-Based Models

Gao, R., Nijkamp, E., Kingma, D. P., Xu, Z. X., Dai, A. M., & Wu, Y. N. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00754 · Cited by 65 · see citing works

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

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

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Learning Energy-Based Models by Diffusion Recovery LikelihoodPreprint

Gao, R., Song, Y. S., Poole, B., Wu, Y., & Kingma, D. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.08125 · Cited by 51 · see citing works

Variational Wasserstein Clustering

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Improved Contrastive Divergence Training of Energy Based Models

Du, Y., Li, S., Tenenbaum, J., & Mordatch, I. (2020) · arXiv · doi:10.48550/arxiv.2012.01316 · Cited by 36 · see citing works

On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models

Nijkamp, E., Hill, M., Han, T., Zhu, S. C., & Wu, Y. N. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5973 · Cited by 38 · see citing works

Adversarial Feedback Loop

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Single image super-resolution approaches in medical images based-deep learning: a survey

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Representation Learning: A Statistical Perspective

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3D Volumetric Modeling with Introspective Neural Networks

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Optical and SAR images-based image translation for change detection using generative adversarial network (GAN)

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TokenCompose: Text-to-Image Diffusion with Token-Level Supervision

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

Zhao, Y., Xie, J., & Li, P. (2021) · International Conference on Learning Representations · Cited by 15 · see citing works

Learning Data Representations with Joint Diffusion Models

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EGC: Image Generation and Classification via a Diffusion Energy-Based Model

Guo, Q., Ma, C., Jiang, Y., Yuan, Z., Yu, Y., & Luo, P. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.02098 · Cited by 8 · see citing works

Compositional Visual Generation and Inference with Energy Based ModelsPreprint

Du, Y., Li, S., & Mordatch, I. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.06030 · Cited by 6 · see citing works

Hybrid Generative-Contrastive Representation LearningPreprint

Kim, S., Kim, S., & Lee, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.06162 · Cited by 6 · see citing works

Deep Generative Knowledge Distillation by Likelihood Finetuning

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CLOT: Closed Loop Optimal Transport for Unsupervised Action Segmentation

Bueno-Benito, E., & Dimiccoli, M. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00998 · Cited by 3 · see citing works

An Introspective Learning Strategy for Remote Sensing Scene Classification

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Dual Contradistinctive Generative AutoencoderPreprint

Parmar, G., Li, D., Lee, K., & Zhuowen Tu, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.10063 · Cited by 3 · see citing works

Efficient learning of Scale-Adaptive Nearly Affine Invariant Networks

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Generative Adversarial Data ProgrammingPreprint

Pal, A., & Balasubramanian, V. N. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2005.00364 · Cited by 2 · see citing works

JointDiffusion: Joint representation learning for generative, predictive, and self-explainable AI in healthcare

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FRAME Revisited: An Interpretation View Based on Particle Evolution

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Resisting Large Data Variations via Introspective Transformation Network

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Synthesize-It-Classifier: Learning a Generative Classifier through Recurrent Self-analysis

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Deep Consensus LearningPreprint

Sun, W., & Wu, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.08475 · Cited by 2 · see citing works

Spatial Transformer Introspective Neural Network.Preprint

Zhao, Y., Tian, Y., Shen, W., & Yuille, A. (2018) · arXiv (Cornell University)

Generalizing Energy-based Generative ConvNets from Particle Evolution PerspectivePreprint

Wu, Y., Xu, C. X., Wei, P., Li, G., & Lin, L. (2019) · arXiv (Cornell University)

Representation Learning: A Statistical PerspectivePreprint

Xie, J., Gao, R., Nijkamp, E., Zhu, S. C., & Wu, Y. N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.11374

Learning to Represent and Predict Sets with Deep Neural NetworksPreprint

Zhang, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.04957

Peer Review #4 of "A comprehensive review of deep learning-based single image super-resolution (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.621v0.1/reviews/4

Peer Review #1 of "A comprehensive review of deep learning-based single image super-resolution (v0.2)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.621v0.2/reviews/1

Peer Review #4 of "A comprehensive review of deep learning-based single image super-resolution (v0.2)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.621v0.2/reviews/4

Peer Review #1 of "A comprehensive review of deep learning-based single image super-resolution (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.621v0.1/reviews/1

Trust the Critics: Generatorless and Multipurpose WGANs with Initial Convergence GuaranteesPreprint

Milne, T., Bilocq, É., & Nachman, A. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.15099

Peer Review #3 of "A comprehensive review of deep learning-based single image super-resolution (v0.2)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.621v0.2/reviews/3

Peer Review #2 of "A comprehensive review of deep learning-based single image super-resolution (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.621v0.1/reviews/2

Peer Review #2 of "A comprehensive review of deep learning-based single image super-resolution (v0.2)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.621v0.2/reviews/2

Peer Review #3 of "A comprehensive review of deep learning-based single image super-resolution (v0.1)"

(2021) · PeerJ Computer Science · doi:10.7287/peerj-cs.621v0.1/reviews/3

Guess-It-Generator

Pal, A., Rajanala, S., Phan, R. C. ‐., & Wong, K. (2022) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3503161.3548393

A Tale of Three Families: Discriminative, Descriptive, and Generative Models

Zhu, S. C., & Wu, Y. N. (2023) · Springer eBooks · doi:10.1007/978-3-030-96530-3_12

Adapt & Align: Continual Learning with Generative Models’ Latent Space Alignment

Deja, K. R., Cywiński, B., Rybarczyk, J., & Trzcinski, T. P. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130748

Introspective Neural Networks for Generative Modeling

Lazarow, J., Jin, L., & Tu, Z. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.302 · Cited by 49 · see citing works

Preserving Semantic Structural Constraints within Neural Networks

Le, H. H. (2018) · eScholarship (California Digital Library)

Wasserstein-Bounded Generative Adversarial Networks

Zhou, P., Ni, B., Xie, L., Zhang, X., Wang, H., Geng, C., & Tian, Q. (2019)

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Using Wasserstein GAN to generate high quality adversarial examples

Xiong, Z. (2018) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)