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Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content.
On the numerical solution of a class of stackelberg problems
Jiří V Outrata · 1990
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Optimality conditions for bilevel programming problems
Jane J Ye and DL Zhu · 1995
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Calibrating noise to sensitivity in private data analysis
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith · 2006
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Incremental and decremental learning for linear support vector machines
Enrique Romero, Ignacio Barrio, and Lluís Belanche · 2007
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Differential privacy: A survey of results
Cynthia Dwork · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Multiple incremental decremental learning of support vector machines
Masayuki Karasuyama and Ichiro Takeuchi · 2010
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Towards making systems forget with machine unlearning
Yinzhi Cao and Junfeng Yang · 2015
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Gradient-based hyperparameter optimization through reversible learning
Dougal Maclaurin, David Duvenaud, and Ryan Adams · 2015
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Deep learning with differential privacy
Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 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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The eu general data protection regulation (gdpr)
Paul Voigt and Axel Von dem Bussche · 2017
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Assessing generative models via precision and recall
Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
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Nudenet: Neural nets for nudity classification, detection and selective censoring, 2019
P Bedapudi · 2019
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Making ai forget you: Data deletion in machine learning
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou · 2019
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Improved precision and recall metric for assessing generative models
Tuomas Kynkäänniemi, Tero Karras, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2019
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Meta-learning with implicit gradients
Aravind Rajeswaran, Chelsea Finn, Sham M Kakade, and Sergey Levine · 2019
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Scalable detection of offensive and non-compliant content/logo in product images
Shreyansh Gandhi, Samrat Kokkula, Abon Chaudhuri, Alessandro Magnani, Theban Stanley, Behzad Ahmadi, Venkatesh Kandaswamy, Omer Ovenc, and Shie Mannor · 2020
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Eternal sunshine of the spotless net: Selective forgetting in deep networks
Aditya Golatkar, Alessandro Achille, and Stefano Soatto · 2020
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An introduction to the california consumer privacy act (ccpa)
Eric Goldman · 2020
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Certified data removal from machine learning models
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Fastai: A layered api for deep learning
Jeremy Howard and Sylvain Gugger · 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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Deep unlearning via randomized conditionally independent hessians
Ronak Mehta, Sourav Pal, Vikas Singh, and Sathya N. Ravi · 2022
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Red-teaming the stable diffusion safety filter
Javier Rando, Daniel Paleka, David Lindner, Lennard Heim, and Florian Tramèr · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Can machines help us answering question 16 in datasheets, and in turn reflecting on inappropriate content?
Patrick Schramowski, Christopher Tauchmann, and Kristian Kersting · 2022
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Laion-5b: An open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al · 2022
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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DeltaGrad: Rapid retraining of machine learning models
Yinjun Wu, Edgar Dobriban, and Susan Davidson · 2020
Cited alongside, same era.
Large image datasets: A pyrrhic win for computer vision?
Abeba Birhane and Vinay Uday Prabhu · 2021
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Machine unlearning
Lucas Bourtoule, Varun Chandrasekaran, Christopher A. Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 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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Diffusion art or digital forgery? investigating data replication in diffusion models
Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2022
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Memorization without overfitting: Analyzing the training dynamics of large language models
Kushal Tirumala, Aram Markosyan, Luke Zettlemoyer, and Armen Aghajanyan · 2022
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Learning with recoverable forgetting
Jingwen Ye, Yifang Fu, Jie Song, Xingyi Yang, Songhua Liu, Xin Jin, Mingli Song, and Xinchao Wang · 2022
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Extracting training data from diffusion models
Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Chongyu Fan, Jiancheng Liu, Yihua Zhang, Dennis Wei, Eric Wong, and Sijia Liu · 2023
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Erasing concepts from diffusion models
Rohit Gandikota, Joanna Materzynska, Jaden Fiotto-Kaufman, and David Bau · 2023
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Ablating concepts in text-to-image diffusion models
Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, and Jun-Yan Zhu · 2023
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Raising the cost of malicious ai-powered image editing
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, and Aleksander Madry · 2023
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Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models
Patrick Schramowski, Manuel Brack, Björn Deiseroth, and Kristian Kersting · 2023
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Glaze: Protecting artists from style mimicry by text-to-image models
Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, and Ben Y Zhao · 2023
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Provable copyright protection for generative models
Nikhil Vyas, Sham Kakade, and Boaz Barak · 2023
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Forget-me-not: Learning to forget in text-to-image diffusion models
Eric Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi · 2023
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One-dimensional adapter to rule them all: Concepts diffusion models and erasing applications
Mengyao Lyu, Yuhong Yang, Haiwen Hong, Hui Chen, Xuan Jin, Yuan He, Hui Xue, Jungong Han, and Guiguang Ding · 2024
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Unlearncanvas: A stylized image dataset to benchmark machine unlearning for diffusion models
Yihua Zhang, Yimeng Zhang, Yuguang Yao, Jinghan Jia, Jiancheng Liu, Xiaoming Liu, and Sijia Liu · 2024
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