A Connection Between Score Matching and Denoising Autoencoders

Advances in neural information processing systems 7

Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,089 · see citing works

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390294 · Cited by 7,436 · see citing works

Training Products of Experts by Minimizing Contrastive Divergence

Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 3× · see citing works

Greedy layer-wise training of deep networks

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841 · see citing works

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works

Hybrid Monte Carlo

Duane, S., Kennedy, A. D., Pendleton, B. J., & Roweth, D. (1987) · Physics Letters B · doi:10.1016/0370-2693(87)91197-x · Cited by 3,920 · see citing works

Why Does Unsupervised Pre-training Help Deep Learning?

Erhan, D., Bengio, Y., Courville, A. C., Manzagol, P.-A., Vincent, P., & Samy Bengio, A. (2010) · Cited by 2,180 · see citing works

Advances in Neural Information Processing Systems 18

Murray, I., Mackay, D., Zoubin Ghahramani, A., & Skilling, J. (2006) · Cited by 1,927 · see citing works

Advances in Neural Information Processing Systems 21

Griffiths, T. L., Lucas, C. G., Williams, J. J., & Kalish, M. L. (2009) · neural information processing systems · Cited by 1,854 · see citing works

Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

Adams, R. P., Dahl, G. E., & Murray, I. (2010) · Cited by 943 · see citing works

Estimation of Non-Normalized Statistical Models by Score Matching

Hyvärinen, A. (2005) · doi:10.5555/1046920.1088696 · Cited by 756 · see citing works

Exponential Family Harmoniums with an Application to Information Retrieval

Welling, M., Rosen‐Zvi, M., & Hinton, G. E. (2004) · Cited by 423 · see citing works

Some extensions of score matching

Hyvärinen, A. (2006) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.09.003 · Cited by 174 · see citing works

Inductive Principles for Restricted Boltzmann Machine Learning

Marlin, B. M., Swersky, K., Chen, B., & de Freitas, N. (2010) · Cited by 133 · see citing works

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010

Sutton, C. A., & Jordan, M. I. (2010) · Cited by 97 · see citing works

A New Learning Algorithm for Mean Field Boltzmann Machines

Welling, M., & Hinton, G. E. (2002) · Lecture notes in computer science · doi:10.1007/3-540-46084-5_57 · Cited by 76 · see citing works

Learning Continuous Attractors in Recurrent Networks

Seung, H. S. (1997) · Cited by 76 · see citing works

Interpretation and Generalization of Score MatchingPreprint

Lyu, S. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2629 · Cited by 68 · see citing works

Connections Between Score Matching, Contrastive Divergence, and Pseudolikelihood for Continuous-Valued Variables

Hyvärinen, A. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2007.895819 · Cited by 52 · see citing works

Regularized estimation of image statistics by Score Matching

Kingma, D. P., & LeCun, Y. (2010) · neural information processing systems · Cited by 52 · see citing works

Minimum Probability Flow LearningPreprint

Sohl‐Dickstein, J., Battaglino, P., & DeWeese, M. R. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0906.4779 · Cited by 45 · see citing works

Optimal Approximation of Signal Priors

Hyvärinen, A. (2008) · Neural Computation · doi:10.1162/neco.2008.10-06-384 · Cited by 22 · see citing works

Memoires associatives distribuees: Une comparaison (Distributed associative memories: A comparison)

Gallinari, P., LeCun, Y., Thiria, S., & Soulié, F. F. (1987) · Cited by 15 · see citing works

Inductive principles for learning Restricted Boltzmann Machines

Swersky, K. (2010) · cIRcle (University of British Columbia) · doi:10.14288/1.0051929 · Cited by 9 · see citing works

Contrastive Divergence in Gaussian Diffusions

Movellan, J. R. (2008) · Neural Computation · doi:10.1162/neco.2008.01-07-430 · Cited by 5 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Denoising Diffusion Probabilistic Models

Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works

A survey of deep neural network architectures and their applications

Liu, W., Wang, Z., Liu, X., Zeng, N., Liu, Y., & Alsaadi, F. E. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.12.038 · Cited by 3,239 · see citing works

Denoising Diffusion Implicit Models

Song, J., Meng, C., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2010.02502 · Cited by 2,507 · see citing works

Practical Recommendations for Gradient-Based Training of Deep Architectures

Bengio, Y. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_26 · Cited by 2,259 · see citing works

Diffusion Models in Vision: A Survey

Croitoru, F.-A., Hondru, V., Ionescu, R. T., & Shah, M. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3261988 · Cited by 1,965 · see citing works

Score-Based Generative Modeling through Stochastic Differential Equations

Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., & Poole, B. (2020) · arXiv · doi:10.48550/arxiv.2011.13456 · Cited by 1,953 · see citing works

Image Super-Resolution Via Iterative Refinement

Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., & Norouzi, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3204461 · Cited by 1,829 · see citing works

Deep Learning: Methods and Applications

Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,785 · see citing works

Diffusion Models: A Comprehensive Survey of Methods and Applications

Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704 · see citing works

Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach

Glorot, X., Bordes, A., & Bengio, Y. (2011) · Cited by 1,569 · see citing works

Generative Modeling by Estimating Gradients of the Data Distribution

Song, Y., & Ermon, S. (2019) · arXiv · doi:10.48550/arxiv.1907.05600 · Cited by 1,453 · see citing works

Deep learning modelling techniques: Current progress, applications, advantages, and challenges

Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079 · see citing works

Neural Networks and Deep Learning

Theodoridis, S. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-801522-3.00018-5 · Cited by 942 · see citing works

Diffusion models in medical imaging: A comprehensive survey

Kazerouni, A., Aghdam, E. K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., & Merhof, D. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102846 · Cited by 850 · see citing works

Imagic: Text-Based Real Image Editing with Diffusion Models

Kawar, B., Zada, S., Lang, O., Tov, O., Chang, H., Dekel, T., Mosseri, I., & Irani, M. (2023) · 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/cvpr52729.2023.00582 · Cited by 813 · see citing works

Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models

Bond-Taylor, S., Leach, A., Long, Y., & Willcocks, C. G. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3116668 · Cited by 735 · see citing works

Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models

Blattmann, A., Rombach, R., Ling, H., Dockhorn, T., Kim, S. W., Fidler, S., & Kreis, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.02161 · Cited by 724 · see citing works

A Survey on Generative Diffusion Models

Cao, H., Tan, C., Gao, Z., Xu, Y., Chen, G., Heng, P., & Li, S. Z. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3361474 · Cited by 679 · see citing works

ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models

Choi, J., Kim, S., Jeong, Y., Gwon, Y., & Yoon, S. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01410 · Cited by 665 · see citing works

Deep Learning of Representations: Looking Forward

Bengio, Y. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39593-2_1 · Cited by 558 · see citing works

Restoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models

Özdenizci, O., & Legenstein, R. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3238179 · Cited by 483 · see citing works

Improved Techniques for Training Score-Based Generative Models

Song, Y., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2006.09011 · Cited by 482 · see citing works

A deep learning approach to structured signal recovery

Mousavi, A., Patel, A. B., & Baraniuk, R. G. (2015) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2015.7447163 · Cited by 479 · see citing works

Deep Anomaly Detection Using Geometric TransformationsPreprint

Golan, I., & El‐Yaniv, R. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.10917 · Cited by 479 · see citing works

A review of deep learning with special emphasis on architectures, applications and recent trends

Sengupta, S., Basak, S., Saikia, P., Paul, S., Tsalavoutis, V. A., Atiah, F. D., Ravi, V., & Peters, A. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105596 · Cited by 468 · see citing works

Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives

Bengio, Y., Courville, A. C., & Vincent, P. (2012) · Cited by 455 · see citing works

Score-based diffusion models for accelerated MRI

Chung, H., & Ye, J. C. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102479 · Cited by 436 · see citing works

WaveGrad: Estimating Gradients for Waveform GenerationPreprint

Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., & Chan, W. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2009.00713 · Cited by 410 · see citing works

Variational Diffusion Models

Kingma, D. P., Salimans, T., Poole, B., & Ho, J. (2021) · arXiv · doi:10.48550/arxiv.2107.00630 · Cited by 374 · see citing works

Amortised MAP Inference for Image Super-resolutionPreprint

Sønderby, C. K., Caballero, J., Theis, L., Shi, W., & Huszár, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.04490 · Cited by 364 · see citing works

Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation

Wang, H., Du, X., Li, J., Yeh, R. A., & Shakhnarovich, G. (2023) · 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/cvpr52729.2023.01214 · Cited by 355 · see citing works

Auto-Encoders in Deep Learning—A Review with New Perspectives

Chen, S., & Guo, W. (2023) · Mathematics · doi:10.3390/math11081777 · Cited by 341 · see citing works

Structured Denoising Diffusion Models in Discrete State-SpacesPreprint

Austin, J., Johnson, D. D., Ho, J., Tarlow, D., & van den Berg, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2107.03006 · Cited by 340 · see citing works

Deep Structured Energy Based Models for Anomaly Detection

Zhai, S., Cheng, Y., Lu, W., & Zhang, Z. (2016) · arXiv · doi:10.48550/arxiv.1605.07717 · Cited by 334 · see citing works

What Regularized Auto-Encoders Learn from the Data Generating DistributionPreprint

Alain, G., & Bengio, Y. (2013) · Cited by 319 · see citing works

High-resolution image reconstruction with latent diffusion models from human brain activity

Takagi, Y., & Nishimoto, S. (2023) · 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/cvpr52729.2023.01389 · Cited by 314 · see citing works

Compositional Visual Generation with Composable Diffusion Models

Liu, N., Li, S., Du, Y., Torralba, A. B., & Tenenbaum, J. B. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-19790-1_26 · Cited by 302 · see citing works

Generalized Denoising Auto-Encoders as Generative Models

Bengio, Y., Yao, L., Alain, G., & Vincent, P. (2013) · arXiv · doi:10.48550/arxiv.1305.6663

Solving Inverse Problems in Medical Imaging with Score-Based Generative ModelsPreprint

Song, Y. S., Shen, L., Xing, L., & Ermon, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.08005 · Cited by 287 · see citing works

ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning

Le, Q. V., Karpenko, A., Ngiam, J., & Ng, A. Y. (2011) · Cited by 283 · see citing works

Speech Enhancement and Dereverberation With Diffusion-Based Generative Models

Richter, J., Welker, S., Lemercier, J.-M., Lay, B., & Gerkmann, T. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3285241 · Cited by 256 · see citing works

PhysDiff: Physics-Guided Human Motion Diffusion Model

Yuan, Y., Song, J., Iqbal, U., Vahdat, A., & Kautz, J. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01467 · Cited by 246 · see citing works

Pylearn2: a machine learning research libraryPreprint

Goodfellow, I., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R., Bergstra, J., Bastien, F., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1308.4214 · Cited by 245 · see citing works

Regularization by Denoising: Clarifications and New Interpretations

Reehorst, E. T., & Schniter, P. (2018) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2018.2880326 · Cited by 241 · see citing works

DiffSinger: Singing Voice Synthesis via Shallow Diffusion Mechanism

Liu, J., Li, C., Ren, Y., Chen, F., & Zhao, Z. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21350 · Cited by 236 · see citing works

Learning Gradient Fields for Shape Generation

Cai, R., Yang, G., Averbuch-Elor, H., Hao, Z., Belongie, S., Snavely, N., & Hariharan, B. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58580-8_22 · Cited by 225 · see citing works

Score-based Generative Modeling in Latent Space

Vahdat, A., Kreis, K., & Kautz, J. (2021) · arXiv · doi:10.48550/arxiv.2106.05931 · Cited by 224 · see citing works

On the Expressive Power of Deep Architectures

Bengio, Y., & Delalleau, O. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24477-3_1 · Cited by 209 · see citing works

Perception Prioritized Training of Diffusion Models

Choi, J., Lee, J., Shin, C., Kim, S., Kim, H., & Yoon, S. (2022) · 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/cvpr52688.2022.01118 · Cited by 205 · see citing works

Deep Learning for Time Series Forecasting: Advances and Open Problems

Casolaro, A., Capone, V., Iannuzzo, G., & Camastra, F. (2023) · Information · doi:10.3390/info14110598 · Cited by 199 · see citing works

Maximum Likelihood Training of Score-Based Diffusion ModelsPreprint

Song, Y. S., Durkan, C., Murray, I., & Ermon, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.09258 · Cited by 197 · see citing works

Diffusion-based Generation, Optimization, and Planning in 3D Scenes

Huang, S., Wang, Z., Li, P., Jia, B., Liu, T., Zhu, Y., Liang, W., & Zhu, S.-C. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01607 · Cited by 192 · see citing works

On the Expressive Power of Deep Architectures

Bengio, Y., & Delalleau, O. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-24412-4_3 · Cited by 182 · see citing works

One-Step Diffusion with Distribution Matching Distillation

Yin, T., Gharbi, M., Zhang, R., Shechtman, E., Durand, F., Freeman, W. T., & Park, T. (2024) · 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/cvpr52733.2024.00632 · Cited by 179 · see citing works

Predicting equilibrium distributions for molecular systems with deep learning

Zheng, S., He, J., Liu, C., Shi, Y., Lu, Z., Feng, W., Ju, F., Wang, J., Zhu, J., Min, Y., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00837-3 · Cited by 178 · see citing works

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

ECG signal enhancement based on improved denoising auto-encoder

Xiong, P., Wang, H., Liu, M., Zhou, S., Hou, Z., & Liu, X. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.02.015 · Cited by 174 · see citing works

BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language ModelPreprint

Wang, A., & Cho, K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.04094 · Cited by 174 · see citing works

Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series ForecastingPreprint

Rasul, K., Seward, C., Schuster, I., & Vollgraf, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.12072 · Cited by 174 · see citing works

Diffusion Model-Based Image Editing: A Survey

Huang, Y., Huang, J., Liu, Y., Yan, M., Lv, J., Liu, J., Xiong, W., Zhang, H., Cao, L., & Chen, S. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3541625 · Cited by 168 · see citing works

Deep AutoRegressive NetworksPreprint

Gregor, K., Danihelka, I., Mnih, A., Blundell, C., & Wierstra, D. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.8499 · Cited by 166 · see citing works

Scheduled denoising autoencodersPreprint

Geras, K. J., & Sutton, C. A. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1406.3269 · Cited by 165 · see citing works

Image Denoising: The Deep Learning Revolution and Beyond—A Survey Paper

Elad, M., Kawar, B., & Vaksman, G. (2023) · SIAM Journal on Imaging Sciences · doi:10.1137/23m1545859 · Cited by 164 · see citing works

Sobolev Training for Neural NetworksPreprint

Czarnecki, W. M., Osindero, S., Jaderberg, M., Świrszcz, G., & Pascanu, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04859 · Cited by 164 · see citing works

Diffusion models in bioinformatics and computational biology

Guo, Z., Liu, J., Wang, Y., Chen, M., Wang, D., Xu, D., & Cheng, J. (2023) · Nature Reviews Bioengineering · doi:10.1038/s44222-023-00114-9 · Cited by 163 · see citing works

Your Diffusion Model is Secretly a Zero-Shot Classifier

Li, A. C., Prabhudesai, M., Duggal, S., Brown, E. V., & Pathak, D. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00210 · Cited by 159 · see citing works

Conditional Image Generation with Score-Based Diffusion ModelsPreprint

Batzolis, G., Stanczuk, J., Schönlieb, C., & Etmann, C. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2111.13606 · Cited by 157 · see citing works

Solving 3D Inverse Problems Using Pre-Trained 2D Diffusion Models

Chung, H., Ryu, D., McCann, M. T., Klasky, M., & Ye, J. C. (2023) · 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/cvpr52729.2023.02159 · Cited by 156 · see citing works

State of the Art on Diffusion Models for Visual Computing

Po, R., Yifan, W., Golyanik, V., Aberman, K., Barron, J. T., Bermano, A. H., Chan, E. R., Dekel, T., Holynski, A., Kanazawa, A., et al. (2024) · Computer Graphics Forum · doi:10.1111/cgf.15063 · Cited by 156 · see citing works

MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion

Chung, H., Lee, E. S., & Ye, J. C. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3220681 · Cited by 155 · see citing works

Collaborative Diffusion for Multi-Modal Face Generation and Editing

Huang, Z., Chan, K. C. K., Jiang, Y., & Liu, Z. (2023) · 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/cvpr52729.2023.00589 · Cited by 155 · see citing works

Deep Computation Model for Unsupervised Feature Learning on Big Data

Zhang, Q., Yang, L. T., & Chen, Z. (2015) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2015.2497705 · Cited by 152 · see citing works

Versatile Diffusion: Text, Images and Variations All in One Diffusion Model

Xu, X., Wang, Z., Zhang, E., Wang, K., & Shi, H. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00713 · Cited by 150 · see citing works

Wavelet Diffusion Models are fast and scalable Image Generators

Phung, H., Dao, Q. T., & Tran, A. N. (2023) · 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/cvpr52729.2023.00983 · Cited by 145 · see citing works

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Çelik, A., & Eltawil, A. M. (2024) · IEEE Open Journal of the Communications Society · doi:10.1109/ojcoms.2024.3362271 · Cited by 145 · see citing works

Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods

Hou, C. K. J., & Behdinan, K. (2022) · Data Science and Engineering · doi:10.1007/s41019-022-00193-5 · Cited by 142 · see citing works

WaveDM: Wavelet-Based Diffusion Models for Image Restoration

Huang, Y., Huang, J., Liu, J., Yan, M., Yu, D., Lv, J., Chen, C., & Chen, S. (2024) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2024.3359769 · Cited by 142 · see citing works

Opportunities and challenges of diffusion models for generative AI

Chen, M., Song, M., Fan, J., & Wang, M. (2024) · National Science Review · doi:10.1093/nsr/nwae348 · Cited by 141 · see citing works

UNIT-DDPM: UNpaired Image Translation with Denoising Diffusion Probabilistic ModelsPreprint

Sasaki, H., Willcocks, C. G., & Breckon, T. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.05358 · Cited by 140 · see citing works

Diffusion models for time-series applications: a survey

Lin, L., Li, Z., Li, R., Li, X., & Gao, J. (2023) · Frontiers of Information Technology & Electronic Engineering · doi:10.1631/fitee.2300310 · Cited by 139 · see citing works

Sliced Score Matching: A Scalable Approach to Density and Score EstimationPreprint

Song, Y. S., Garg, S., Shi, J., & Ermon, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07088 · Cited by 134 · see citing works

How to Train Your Energy-Based ModelsPreprint

Song, Y., & Kingma, D. P. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.03288 · Cited by 132 · see citing works

StoRM: A Diffusion-Based Stochastic Regeneration Model for Speech Enhancement and Dereverberation

Lemercier, J.-M., Richter, J., Welker, S., & Gerkmann, T. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3294692 · Cited by 131 · see citing works

Toward High-Quality HDR Deghosting With Conditional Diffusion Models

Yan, Q., Hu, T., Sun, Y., Tang, H., Zhu, Y., Dong, W. F., Gool, L. V., & Zhang, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3326293 · Cited by 131 · see citing works

MIMO Channel Estimation Using Score-Based Generative Models

Arvinte, M., & Tamir, J. I. (2022) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2022.3220784 · Cited by 129 · see citing works

Deep Learning of Representations: Looking ForwardPreprint

Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1305.0445 · Cited by 128 · see citing works

How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target PropagationPreprint

Bengio, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1407.7906 · Cited by 127 · see citing works

Knowledge Distillation in Iterative Generative Models for Improved Sampling SpeedPreprint

Luhman, E., & Luhman, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02388 · Cited by 125 · see citing works

Distributed Compressive Sensing: A Deep Learning Approach

Palangi, H., Ward, R. K., & Deng, L. (2016) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2016.2557301 · Cited by 122 · see citing works

Score-based generative models for calorimeter shower simulation

Mikuni, V. M., & Nachman, B. P. (2022) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.106.092009 · Cited by 121 · see citing works

Denoising Pretraining for Semantic Segmentation

Brempong, E. A., Kornblith, S., Chen, T., Parmar, N., Minderer, M., & Norouzi, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw56347.2022.00462 · Cited by 120 · see citing works

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

Zvyagin, M., Brace, A., Hippe, K., Deng, Y., Zhang, B., Bohorquez, C. O., Clyde, A., Kale, B., Perez-Rivera, D., Ma, H., et al. (2023) · The International Journal of High Performance Computing Applications · doi:10.1177/10943420231201154 · Cited by 120 · see citing works

GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics

Zvyagin, M., Brace, A., Hippe, K., Deng, Y., Zhang, B., Bohorquez, C. O., Clyde, A., Kale, B., Perez-Rivera, D., Ma, H., et al. (2022) · doi:10.1101/2022.10.10.511571 · Cited by 120 · see citing works

Structural health and condition monitoring via electrical impedance tomography in self-sensing materials: a review

Tallman, T. N., & Smyl, D. (2020) · Smart Materials and Structures · doi:10.1088/1361-665x/abb352 · Cited by 120 · see citing works

Diffusion Models, Image Super-Resolution, and Everything: A Survey

Moser, B. B., Shanbhag, A. S., Raue, F., Frolov, S., Palacio, S., & Dengel, A. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2024.3476671 · Cited by 117 · see citing works

Deep Learners Benefit More from Out-of-Distribution Examples

Bengio, Y., Bastien, F., Bergeron, A., Boulanger-Lewandowski, N., Breuel, T. M., Chherawala, Y., Cissé, M., Côté, M., Erhan, D., Eustache, J., et al. (2011) · Cited by 117 · see citing works

A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Images

Moghadam, P. A., Dalen, S. V., Martin, K. C., Lennerz, J. K., Yip, S. S. F., Farahani, H., & Bashashati, A. (2023) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv56688.2023.00204 · Cited by 117 · see citing works

DiffusioNeRF: Regularizing Neural Radiance Fields with Denoising Diffusion Models

Wynn, J., & Turmukhambetov, D. (2023) · 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/cvpr52729.2023.00407 · Cited by 111 · see citing works

Diffusion Models in Low-Level Vision: A Survey

He, C., Shen, Y., Fang, C., Xiao, F., Tang, L., Zhang, Y., Zuo, W., Guo, Z., & Li, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3545047 · Cited by 109 · see citing works

A connection between Score Matching

Autoencoders, D., & Vincent, P. (2010)

On Autoencoders and Score Matching for Energy Based Models

Swersky, K., Ranzato, M., Buchman, D., de Freitas, N., & Marlin, B. M. (2011) · Cited by 76 · see citing works

Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional SpacesPreprint

Li, Z., Chen, Y., & Sommer, F. T. (2019) · arXiv (Cornell University) · Cited by 13 · see citing works

On autoencoder scoring

Kamyshanska, H., & Memisevic, R. (2013) · Cited by 24 · see citing works

Optimizing Input of Denoising Score Matching is Biased Towards Higher Score NormPreprint

Xu, T. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2511.11727