Most cited papers in Generative Adversarial Networks and Image Synthesis
What are the most cited papers in Generative Adversarial Networks and Image Synthesis?
The most cited paper in Generative Adversarial Networks and Image Synthesis is "Deep learning" (Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton, 2015), with 84,939 citations. Next come "Learning Multiple Layers of Features from Tiny Images" (25,341) and "Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks" (22,468).
Showing 50 of 100 papers. Every paper with more than 1,440 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep learning Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton · Nature | 26 May 2015 | 84,939 |
| 2 | Learning Multiple Layers of Features from Tiny Images Alex Krizhevsky | 2024 | 25,341 |
| 3 | Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks Jun-Yan Zhu, Taesung Park, Phillip Isola et al. | Oct 2017 | 22,468 |
| 4 | SSD: Single Shot MultiBox Detector Wei Liu, Dragomir Anguelov, Dumitru Erhan et al. · Lecture notes in computer science | 2016 | 21,849 |
| 5 | GAN（Generative Adversarial Nets） 柴田 淳司 · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics | 15 Oct 2017 | 21,691 |
| 6 | A Fast Learning Algorithm for Deep Belief Nets Geoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh · Neural Computation | 29 May 2006 | 16,607 |
| 7 | High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach, Andreas Blattmann, Dominik Lorenz et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Jun 2022 | 15,702 |
| 8 | Visualizing and Understanding Convolutional Networks Matthew D. Zeiler, Rob Fergus · Lecture notes in computer science | 2014 | 15,691 |
| 9 | Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe · arXiv (Cornell University) | 2024 | 15,638 |
| 10 | Generative adversarial networks Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza et al. · Communications of the ACM | 22 Oct 2020 | 14,417 |
| 11 | Rectified Linear Units Improve Restricted Boltzmann Machines Vinod B Nair, Geoffrey E. Hinton · International Conference on Machine Learning | 21 Jun 2010 | 13,220 |
| 12 | A survey on Image Data Augmentation for Deep Learning Connor Shorten, Taghi M. Khoshgoftaar · Journal Of Big Data | 6 Jul 2019 | 13,152 |
| 13 | Understanding the difficulty of training deep feedforward neural networks Xavier Glorot, Yoshua Bengio | 2010 | 12,654 |
| 14 | Caffe Yangqing Jia, Evan Shelhamer, Jeff Donahue et al. | 3 Nov 2014 | 11,245 |
| 15 | Extracting and composing robust features with denoising autoencoders Pascal Vincent, Hugo Larochelle, Yoshua Bengio et al. | 2008 | 7,452 |
| 16 | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala · arXiv (Cornell University) | 19 Nov 2015 | 6,972 |
| 17 | Image Style Transfer Using Convolutional Neural Networks Leon A. Gatys, Alexander S. Ecker, Matthias Bethge | Jun 2016 | 6,164 |
| 18 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das et al. · International Journal of Computer Vision | 11 Oct 2019 | 6,062 |
| 19 | Denoising Diffusion Probabilistic Models Ho, Jonathan, Ajay N. Jain, Pieter Abbeel · arXiv (Cornell University) | 19 Jun 2020 | 5,696 |
| 20 | Least Squares Generative Adversarial Networks Xudong Mao, Qing Li, Haoran Xie et al. | Oct 2017 | 5,285 |
| 21 | Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization Xun Cheng Huang, Serge Belongie | Oct 2017 | 5,077 |
| 22 | Wasserstein Generative Adversarial Networks Martín Arjovsky, Soumith Chintala, Léon Bottou · International Conference on Machine Learning | 17 Jul 2017 | 5,047 |
| 23 | Deep visual-semantic alignments for generating image descriptions Andrej Karpathy, Li Fei-Fei | Jun 2015 | 5,042 |
| 24 | Greedy Layer-Wise Training of Deep Networks Yoshua Bengio, Pascal Lamblin, Dan Popovici et al. · The MIT Press eBooks | 7 Sep 2007 | 4,753 |
| 25 | Generative Adversarial Networks: An Overview Antonia Creswell, Tom White, Vincent Dumoulin et al. · IEEE Signal Processing Magazine | 2018 | 4,566 |
| 26 | High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu et al. | Jun 2018 | 4,526 |
| 27 | Adding Conditional Control to Text-to-Image Diffusion Models Lvmin Zhang, Anyi Rao, Maneesh Agrawala | Oct 2023 | 4,229 |
| 28 | Variational Inference: A Review for Statisticians David M. Blei, Alp Kucukelbir, Jon D. McAuliffe · Journal of the American Statistical Association | 27 Feb 2017 | 3,893 |
| 29 | Image inpainting Marcelo Bertalmı́o, Guillermo Sapiro, V. Caselles et al. | 2000 | 3,634 |
| 30 | Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han, Huizi Mao, William J. Dally · arXiv (Cornell University) | Oct 2015 | 3,528 |
| 31 | Bootstrap your own latent: A new approach to self-supervised Learning Jean-Bastien Grill, Florian Strub, Florent Altché et al. · arXiv (Cornell University) | 13 Jun 2020 | 3,441 |
| 32 | Deep Learning: Methods and Applications Li Deng, Dong Yu · Foundations and Trends® in Signal Processing | 30 Jun 2014 | 3,405 |
| 33 | Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros · Lecture notes in computer science | 2016 | 3,362 |
| 34 | A survey of deep neural network architectures and their applications Weibo Liu, Zidong Wang, Xiaohui Liu et al. · Neurocomputing | 18 Dec 2016 | 3,298 |
| 35 | Region Filling and Object Removal by Exemplar-Based Image Inpainting Antonio Criminisi, Patrick Pérez, Kentaro Toyama · IEEE Transactions on Image Processing | 16 Aug 2004 | 3,287 |
| 36 | beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework Irina Higgins, Löıc Matthey, Arka Pal et al. · International Conference on Learning Representations | 24 Apr 2017 | 3,076 |
| 37 | Texture synthesis by non-parametric sampling Alexei A. Efros, Thomas Leung | 1999 | 3,061 |
| 38 | PatchMatch Connelly Barnes, Eli Shechtman, Adam Finkelstein et al. · ACM Transactions on Graphics | 27 Jul 2009 | 3,005 |
| 39 | Self-Attentive Sequential Recommendation Wang-Cheng Kang, Julian McAuley | Nov 2018 | 3,000 |
| 40 | Semantic Image Synthesis With Spatially-Adaptive Normalization Taesung Park, Ming-Yu Liu, Ting-Chun Wang et al. | Jun 2019 | 2,926 |
| 41 | Machine Learning for Fluid Mechanics Steven L. Brunton, Bernd R. Noack, Petros Koumoutsakos · Annual Review of Fluid Mechanics | 12 Sep 2019 | 2,842 |
| 42 | Learning Transferable Features with Deep Adaptation Networks Mingsheng Long, Yue Cao, Jianmin Wang et al. · arXiv (Cornell University) | 10 Feb 2015 | 2,838 |
| 43 | A Practical Guide to Training Restricted Boltzmann Machines Geoffrey E. Hinton · Lecture notes in computer science | 2012 | 2,838 |
| 44 | An Introduction to Variational Autoencoders Diederik P. Kingma, Max Welling · Foundations and Trends® in Machine Learning | 28 Nov 2019 | 2,652 |
| 45 | Stochastic Backpropagation and Approximate Inference in Deep Generative Models Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra · arXiv (Cornell University) | 16 Jan 2014 | 2,638 |
| 46 | Multimodal Unsupervised Image-to-Image Translation Xun Cheng Huang, Ming-Yu Liu, Serge Belongie et al. · Lecture notes in computer science | 2018 | 2,591 |
| 47 | Generative Adversarial Networks Iddo Drori · Cambridge University Press eBooks | 18 Aug 2022 | 2,579 |
| 48 | Generative Image Inpainting with Contextual Attention Jiahui Yu, Zhe Lin, Jimei Yang et al. | Jun 2018 | 2,537 |
| 49 | Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations Honglak Lee, Roger Grosse, Rajesh Ranganath et al. | 14 Jun 2009 | 2,462 |
| 50 | Image Inpainting for Irregular Holes Using Partial Convolutions Guilin Liu, Fitsum A. Reda, Kevin J. Shih et al. · Lecture notes in computer science | 2018 | 2,442 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.