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As deep generative models have progressed, recent work has shown them to be capable of memorizing and reproducing training datapoints when deployed.
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Elizaveta Levina and Peter Bickel · 2004
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Alex Krizhevsky and Geoffrey Hinton · 2009
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Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Diederik P Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Theoretical insights into memorization in gans
Vaishnavh Nagarajan, Colin Raffel, and Ian J Goodfellow · 2018
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Diagnosing and enhancing VAE models
Bin Dai and David Wipf · 2019
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Tero Karras, Samuli Laine, and Timo Aila · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
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A non-parametric test to detect data-copying in generative models
Casey Meehan, Kamalika Chaudhuri, and Sanjoy Dasgupta · 2020
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On training sample memorization: Lessons from benchmarking generative modeling with a large-scale competition
Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, and Wendy Chi-wen Kan · 2021
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When do GANs replicate? on the choice of dataset size
Qianli Feng, Chenqi Guo, Fabian Benitez-Quiroz, and Aleix M Martinez · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Learning transferable visual models from natural language supervision
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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On the generalization properties of diffusion models
Puheng Li, Zhong Li, Huishuai Zhang, and Jiang Bian · 2023
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Mathematical analysis of singularities in the diffusion model under the submanifold assumption
Yubin Lu, Zhongjian Wang, and Guillaume Bal · 2023
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Andersen v. Stability AI Ltd., 2023
William H. Orrick · 2023
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Neural implicit manifold learning for topology-aware generative modelling
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L Caterini, and Jesse C Cresswell · 2023
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Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
George Stein, Jesse C Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L Caterini, J Eric T Taylor, and Gabriel Loaiza-Ganem · 2023
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Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Cited alongside, same era.
This person (probably) exists. Identity membership attacks against GAN generated faces
Ryan Webster, Julien Rabin, Loic Simon, and Frederic Jurie · 2021
Cited alongside, same era.
Quantifying memorization across neural language models
Nicholas Carlini, Daphne Ippolito, Matthew Jagielski, Katherine Lee, Florian Tramer, and Chiyuan Zhang · 2022
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Intrinsic dimensionality estimation using normalizing flows
Christian Horvat and Jean-Pascal Pfister · 2022
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Diagnosing and fixing manifold overfitting in deep generative models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C Cresswell, and Anthony L. Caterini · 2022
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DALL ⋅ \cdot E 2 Pre-Training Mitigations, June 2022
Alex Nichol, Aditya Ramesh, Pamela Mishkin, Prafulla Dariwal, Joanne Jang, and Mark Chen · 2022
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Score-based generative models detect manifolds
Jakiw Pidstrigach · 2022
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On provable copyright protection for generative models
Nikhil Vyas, Sham M Kakade, and Boaz Barak · 2023
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A reproducible extraction of training images from diffusion models
Ryan Webster · 2023
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Detecting, explaining, and mitigating memorization in diffusion models
Yuxin Wen, Yuchen Liu, Chen Chen, and Lingjuan Lyu · 2023
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On the generalization of diffusion model
Mingyang Yi, Jiacheng Sun, and Zhenguo Li · 2023
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Diffusion probabilistic models generalize when they fail to memorize
TaeHo Yoon, Joo Young Choi, Sehyun Kwon, and Ernest K Ryu · 2023
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Towards memorization-free diffusion models
Chen Chen, Daochang Liu, and Chang Xu · 2024
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Consistent Diffusion Meets Tweedie: Training Exact Ambient Diffusion Models with Noisy Data
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Finding NeMo: Localizing Neurons Responsible For Memorization in Diffusion Models
Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, and Franziska Boenisch · 2024
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On gauge freedom, conservativity and intrinsic dimensionality estimation in diffusion models
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Understanding the local geometry of generative model manifolds
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Generalization in diffusion models arises from geometry-adaptive harmonic representation
Zahra Kadkhodaie, Florentin Guth, Eero P Simoncelli, and Stéphane Mallat · 2024
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A good score does not lead to a good generative model
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Deep generative models through the lens of the manifold hypothesis: A survey and new connections
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L Caterini, and Jesse C Cresswell · 2024
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Diffusion models encode the intrinsic dimension of data manifolds
Jan Pawel Stanczuk, Georgios Batzolis, Teo Deveney, and Carola-Bibiane Schönlieb · 2024
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Tuxemon Project · 2024
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Beware of diffusion models for synthesizing medical images-a comparison with gans in terms of memorizing brain mri and chest x-ray images
Muhammad Usman Akbar, Wuhao Wang, and Anders Eklund · 2025
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