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Generative models (e.g., GANs, diffusion models) learn the underlying data distribution in an unsupervised manner.
Information theory and statistics: A tutorial
Imre Csiszár and Paul C. Shields · 2004
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, Aurelio Ranzato, and Fu Jie Huang · 2006
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Training restricted Boltzmann machines using approximations to the likelihood gradient
Tijmen Tieleman · 2008
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Bayesian learning via stochastic gradient Langevin dynamics
Max Welling and Yee Whye Teh · 2011
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Generative adversarial nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
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NICE: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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ImageNet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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LSUN: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Learning a model of facial shape and expression from 4D scans
Tianye Li, Timo Bolkart, Michael J. Black, Hao Li, and Javier Romero · 2017
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Plug & play generative networks: Conditional iterative generation of images in latent space
Anh M Nguyen, Jeff Clune, Yoshua Bengio, Alexey Dosovitskiy, and Jason Yosinski · 2017
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Interpretable transformations with encoder-decoder networks
Daniel E. Worrall, Stephan J. Garbin, Daniyar Turmukhambetov, and Gabriel J. Brostow · 2017
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Latent constraints: Learning to generate conditionally from unconditional generative models
Jesse H. Engel, Matthew D. Hoffman, and Adam Roberts · 2018
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Deep generative models with learnable knowledge constraints
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, Xiaodan Liang, Lianhui Qin, Haoye Dong, and Eric P. Xing · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
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Guided image generation with conditional invertible neural networks
Lynton Ardizzone, Carsten Lüth, Jakob Kruse, Carsten Rother, and U. Köthe · 2019
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Seeing what a GAN cannot generate
David Bau, Jun-Yan Zhu, Jonas Wulff, William S. Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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ArcFace: Additive angular margin loss for deep face recognition
Jiankang Deng, J. Guo, and Stefanos Zafeiriou · 2019
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Implicit generation and modeling with energy based models
Yilun Du and Igor Mordatch · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Controllable text-to-image generation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, and Philip H. S. Torr · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Jonas Köhler, and Hao Wu · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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The emergence of Deepfake technology: A review
Mika Westerlund · 2019
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StarGAN v2: Diverse image synthesis for multiple domains
Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha · 2020
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Disentangled and controllable face image generation via 3D imitative-contrastive learning
Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong · 2020
Cited alongside, same era.
Compositional visual generation with energy based models
Yilun Du, Shuang Li, and Igor Mordatch · 2020
Cited alongside, same era.
Your classifier is secretly an energy based model and you should treat it like one
Will Grathwohl, Kuan-Chieh Wang, Joern-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, and Kevin Swersky · 2020
Cited alongside, same era.
Fair generative modeling via weak supervision
Aditya Grover, Kristy Choi, Rui Shu, and Stefano Ermon · 2020
Cited alongside, same era.
GANSpace: Discovering interpretable GAN controls
Erik Härkönen, Aaron Hertzmann, Jaakko Lehtinen, and Sylvain Paris · 2020
GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Controllable and compositional generation with latent-space energy-based models
Weili Nie, Arash Vahdat, and Anima Anandkumar · 2021
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GIRAFFE: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2021
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StyleCLIP: Text-driven manipulation of StyleGAN imagery
Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, and D. Lischinski · 2021
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Learning transferable visual models from natural language supervision
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Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Transforming and projecting images into class-conditional generative networks
Minyoung Huh, Richard Zhang, Jun-Yan Zhu, Sylvain Paris, and Aaron Hertzmann · 2020
Cited alongside, same era.
Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Cited alongside, same era.
12-in-1: Multi-task vision and language representation learning
Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach, Devi Parikh, and Stefan Lee · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text Transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2020
Cited alongside, same era.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Fair attribute classification through latent space de-biasing
Vikram V. Ramaswamy, Sunnie S. Y. Kim, and Olga Russakovsky · 2021
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, and Mohammad Norouzi · 2021
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LAION-400M: Open dataset of CLIP-filtered 400 million image-text pairs
Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki · 2021
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GAN-Control: Explicitly controllable GANs
Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky, and Gérard Medioni · 2021
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Aligning latent and image spaces to connect the unconnectable
Ivan Skorokhodov, Grigorii Sotnikov, and Mohamed Elhoseiny · 2021
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Composing normalizing flows for inverse problems
Jay Whang, Erik M. Lindgren, and Alexandros G. Dimakis · 2021
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GAN inversion: A survey
Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang · 2021
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Learning to prompt for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2021
Later among the works it cites.
StyleGAN-NADA: CLIP-guided domain adaptation of image generators
Rinon Gal, Or Patashnik, Haggai Maron, Gal Chechik, and Daniel Cohen-Or · 2022
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StyleNeRF: A style-based 3D aware generator for high-resolution image synthesis
Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt · 2022
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A distributional lens for multi-aspect controllable text generation
Yuxuan Gu, Xiaocheng Feng, Sicheng Ma, Lingyu Zhang, Heng Gong, and Bing Qin · 2022
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MaGNET: Uniform sampling from deep generative network manifolds without retraining
Ahmed Imtiaz Humayun, Randall Balestriero, and Richard Baraniuk · 2022
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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge J. Belongie, Bharath Hariharan, and Ser Nam Lim · 2022
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FairStyle: Debiasing StyleGAN2 with style channel manipulations
Cemre Karakas, Alara Dirik, Eylül Yalçınkaya, and Pinar Yanardag · 2022
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On aliased resizing and surprising subtleties in GAN evaluation
Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu · 2022
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Diffusion autoencoders: Toward a meaningful and decodable representation
Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn · 2022
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A generalist agent
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, Tom Eccles, Jake Bruce, Ali Razavi, Ashley Edwards, Nicolas Manfred Otto Heess, Yutian Chen, Raia Hadsell, Oriol Vinyals, Mahyar Bordbar, and Nando de Freitas · 2022
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StyleGAN-XL: Scaling StyleGAN to large diverse datasets
Axel Sauer, Katja Schwarz, and Andreas Geiger · 2022
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Generating high fidelity data from low-density regions using diffusion models
Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, and Cristian Canton Ferrer · 2022
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InterFaceGAN: Interpreting the disentangled face representation learned by GANs
Yujun Shen, Ceyuan Yang, Xiaoou Tang, and Bolei Zhou · 2022
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UnifiedSKG: Unifying and multi-tasking structured knowledge grounding with text-to-text language models
Tianbao Xie, Chen Henry Wu, Peng Shi, Ruiqi Zhong, Torsten Scholak, Michihiro Yasunaga, Chien-Sheng Wu, Ming Zhong, Pengcheng Yin, Sida I. Wang, Victor Zhong, Bailin Wang, Chengzu Li, Connor Boyle, Ansong Ni, Ziyu Yao, Dragomir Radev, Caiming Xiong, Lingpeng Kong, Rui Zhang, Noah A. Smith, Luke Zettlemoyer, and Tao Yu · 2022
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Conditional prompt learning for vision-language models
Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu · 2022
Closest in time.