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As diffusion models become increasingly popular, the misuse of copyrighted and private images has emerged as a major concern.
A value for n-person games
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Clustering with bregman divergences
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Pang Wei Koh and Percy Liang · 2017
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A unified approach to interpreting model predictions
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Neural discrete representation learning
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Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, and Pradeep K Ravikumar · 2018
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Input similarity from the neural network perspective
Guillaume Charpiat, Nicolas Girard, Loris Felardos, and Yuliya Tarabalka · 2019
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Data shapley: Equitable valuation of data for machine learning
Amirata Ghorbani and James Zou · 2019
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Towards efficient data valuation based on the shapley value
Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos · 2019
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Interpreting black box predictions using fisher kernels
Rajiv Khanna, Been Kim, Joydeep Ghosh, and Sanmi Koyejo · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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Relatif: Identifying explanatory training samples via relative influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Vitaly Feldman and Chiyuan Zhang · 2020
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Ridge regularization: An essential concept in data science
Trevor Hastie · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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New insights and perspectives on the natural gradient method
James Martens · 2020
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
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Influence functions in deep learning are fragile
Samyadeep Basu, Phil Pope, and Soheil Feizi · 2021
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Hydra: Hypergradient data relevance analysis for interpreting deep neural networks
Yuanyuan Chen, Boyang Li, Han Yu, Pengcheng Wu, and Chunyan Miao · 2021
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Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
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Palette: Image-to-image diffusion models
Chitwan Saharia, William Chan, Huiwen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi · 2022
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Scaling up influence functions
Andrea Schioppa, Polina Zablotskaia, David Vilar, and Artem Sokolov · 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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Training data attribution for diffusion models, 2023
Zheng Dai and David K Gifford · 2023
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The journey, not the destination: How data guides diffusion models
Kristian Georgiev, Joshua Vendrow, Hadi Salman, Sung Min Park, and Aleksander Madry · 2023
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Prompt-to-prompt image editing with cross-attention control
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Scalability vs. utility: Do we have to sacrifice one for the other in data importance quantification?
Ruoxi Jia, Fan Wu, Xuehui Sun, Jiacen Xu, David Dao, Bhavya Kailkhura, Ce Zhang, Bo Li, and Dawn Song · 2021
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Understanding instance-based interpretability of variational auto-encoders
Zhifeng Kong and Kamalika Chaudhuri · 2021
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Influence selection for active learning
Zhuoming Liu, Hao Ding, Huaping Zhong, Weijia Li, Jifeng Dai, and Conghui He · 2021
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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
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Influence estimation for generative adversarial networks
Naoyuki Terashita, Hiroki Ohashi, Yuichi Nonaka, and Takashi Kanemaru · 2021
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Towards tracing knowledge in language models back to the training data
Ekin Akyurek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, and Kelvin Guu · 2022
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Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-or · 2023
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A kernel-based view of language model fine-tuning
Sadhika Malladi, Alexander Wettig, Dingli Yu, Danqi Chen, and Sanjeev Arora · 2023
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Trak: Attributing model behavior at scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry · 2023
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Modeldiff: A framework for comparing learning algorithms
Harshay Shah, Sung Min Park, Andrew Ilyas, and Aleksander Madry · 2023
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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 · 2023
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Evaluating data attribution for text-to-image models
Sheng-Yu Wang, Alexei A Efros, Jun-Yan Zhu, and Richard Zhang · 2023
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Adding conditional control to text-to-image diffusion models
Lvmin Zhang, Anyi Rao, and Maneesh Agrawala · 2023
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Training data influence analysis and estimation: a survey
Zayd Hammoudeh and Daniel Lowd · 2024
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Most influential subset selection: Challenges, promises, and beyond
Yuzheng Hu, Pingbang Hu, Han Zhao, and Jiaqi Ma · 2024
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Interpretable diffusion via information decomposition
Xianghao Kong, Ollie Liu, Han Li, Dani Yogatama, and Greg Ver Steeg · 2024
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Datainf: Efficiently estimating data influence in loRA-tuned LLMs and diffusion models
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Deepcache: Accelerating diffusion models for free
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Intriguing properties of data attribution on diffusion models
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Montrage: Monitoring training for attribution of generative diffusion models
Jonathan Brokman, Omer Hofman, Roman Vainshtein, Amit Giloni, Toshiya Shimizu, Inderjeet Singh, Oren Rachmil, Alon Zolfi, Asaf Shabtai, Yuki Unno, et al · 2025
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Influence functions for scalable data attribution in diffusion models
Bruno Kacper Mlodozeniec, Runa Eschenhagen, Juhan Bae, Alexander Immer, David Krueger, and Richard E. Turner · 2025
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