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The past few years have witnessed substantial advances in image generation powered by diffusion models.
A density-based algorithm for discovering clusters in large spatial databases with noise
Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, et al · 1996
Earlier work this paper cites.
Maximum likelihood estimation of intrinsic dimension
Elizaveta Levina and Peter Bickel · 2004
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Privacy as contextual integrity
Helen Nissenbaum · 2004
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Earlier work this paper cites.
Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
Earlier work this paper cites.
Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Do gans actually learn the distribution? an empirical study
Sanjeev Arora and Yi Zhang · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov · 2017
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Pate-gan: Generating synthetic data with differential privacy guarantees
James Jordon, Jinsung Yoon, and Mihaela Van Der Schaar · 2018
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Progressive growing of GANs for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Theoretical insights into memorization in gans
N Vaishnavh, C Raffel, and IJ Goodfellow · 2018
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Towards gan benchmarks which require generalization
Ishaan Gulrajani, Colin Raffel, and Luke Metz · 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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Detecting overfitting of deep generative networks via latent recovery
Ryan Webster, Julien Rabin, Loic Simon, and Frédéric Jurie · 2019
Cited alongside, same era.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
When do gans replicate? on the choice of dataset size
Qianli Feng, Chenqi Guo, Fabian Benitez-Quiroz, and Aleix M Martinez · 2021
Cited alongside, same era.
Gradient-based adversarial attacks against text transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou, and Douwe Kiela · 2021
Cited alongside, same era.
Are diffusion models vulnerable to membership inference attacks?
Jinhao Duan, Fei Kong, Shiqi Wang, Xiaoshuang Shi, and Kaidi Xu · 2023
Later among the works it cites.
An image is worth one word: Personalizing text-to-image generation using textual inversion
Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit Haim Bermano, Gal Chechik, and Daniel Cohen-or · 2023
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Photographer sues LAION for copyright infringement
Andres Guadamuz · 2023
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Null-text inversion for editing real images using guided diffusion models
Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or · 2023
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Generation of anonymous chest radiographs using latent diffusion models for training thoracic abnormality classification systems
Kai Packhäuser, Lukas Folle, Florian Thamm, and Andreas Maier · 2023
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
Cited alongside, same era.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Cited alongside, same era.
On memorization in probabilistic deep generative models
Gerrit van den Burg and Chris Williams · 2021
Cited alongside, same era.
https://arstechnica.com/information-technology/2022/09/artist-finds-private-medical-record-photos-in-popular-ai-training-data-set/
Artist finds private medical record photos in popular AI training data set · 2022
Cited alongside, same era.
Membership inference attacks from first principles
Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Pamela Samuelson · 2023
Later among the works it cites.
On provable copyright protection for generative models
Nikhil Vyas, Sham M Kakade, and Boaz Barak · 2023
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DIRE for diffusion-generated image detection
Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, and Houqiang Li · 2023
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A reproducible extraction of training images from diffusion models
Ryan Webster · 2023
Later among the works it cites.
On the de-duplication of LAION-2B
Ryan Webster, Julien Rabin, Loic Simon, and Frederic Jurie · 2023
Later among the works it cites.
Inversion-based style transfer with diffusion models
Yuxin Zhang, Nisha Huang, Fan Tang, Haibin Huang, Chongyang Ma, Weiming Dong, and Changsheng Xu · 2023
Later among the works it cites.
Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J Zico Kolter, and Matt Fredrikson · 2023
Later among the works it cites.
Video generation models as world simulators
Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng, Ricky Wang, and Aditya Ramesh · 2024
Closest in time.
Fakeinversion: Learning to detect images from unseen text-to-image models by inverting stable diffusion
George Cazenavette, Avneesh Sud, Thomas Leung, and Ben Usman · 2024
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Towards memorization-free diffusion models
Chen Chen, Daochang Liu, and Chang Xu · 2024
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Unveiling and mitigating memorization in text-to-image diffusion models through cross attention
Jie Ren, Yaxin Li, Shenglai Zeng, Han Xu, Lingjuan Lyu, Yue Xing, and Jiliang Tang · 2024
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Detecting, explaining, and mitigating memorization in diffusion models
Yuxin Wen, Yuchen Liu, Chen Chen, and Lingjuan Lyu · 2024
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Cgi-dm: Digital copyright authentication for diffusion models via contrasting gradient inversion
Xiaoyu Wu, Yang Hua, Chumeng Liang, Jiaru Zhang, Hao Wang, Tao Song, and Haibing Guan · 2024
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Image-level memorization detection via inversion-based inference perturbation
Yue Jiang, Haokun Lin, Yang Bai, Bo Peng, Zhili Liu, Yueming Lyu, Yong Yang, Xingzheng, and Jing Dong · 2025
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