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With significant advancements in diffusion models, addressing the potential risks of dataset bias becomes increasingly important.
Reverse-time diffusion equation models
Brian DO Anderson · 1982
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Learning multiple layers of features from tiny images
Alex Krizhevsky · 2009
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Michael Gutmann and Aapo Hyvärinen · 2010
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Unbiased look at dataset bias
Antonio Torralba and Alexei A Efros · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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Fairness through awareness
Cynthia Dwork, Moritz Hardt, Toniann Pitassi, Omer Reingold, and Richard Zemel · 2012
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Density ratio estimation in machine learning
Masashi Sugiyama, Taiji Suzuki, and Takafumi Kanamori · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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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
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Certifying and removing disparate impact
Michael Feldman, Sorelle A Friedler, John Moeller, Carlos Scheidegger, and Suresh Venkatasubramanian · 2015
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Classification with noisy labels by importance reweighting
Tongliang Liu and Dacheng Tao · 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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The variational fair autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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f-gan: Training generative neural samplers using variational divergence minimization
Sebastian Nowozin, Botond Cseke, and Ryota Tomioka · 2016
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Generative adversarial nets from a density ratio estimation perspective
Masatoshi Uehara, Issei Sato, Masahiro Suzuki, Kotaro Nakayama, and Yutaka Matsuo · 2016
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Towards principled methods for training generative adversarial networks
Martin Arjovsky and Leon Bottou · 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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Stabilizing training of generative adversarial networks through regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin, and Thomas Hofmann · 2017
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A deeper look at dataset bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars · 2017
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Fairness behind a veil of ignorance: A welfare analysis for automated decision making
Hoda Heidari, Claudio Ferrari, Krishna Gummadi, and Andreas Krause · 2018
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Adaptive sensitive reweighting to mitigate bias in fairness-aware classification
Emmanouil Krasanakis, Eleftherios Spyromitros-Xioufis, Symeon Papadopoulos, and Yiannis Kompatsiaris · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Fairgan: Fairness-aware generative adversarial networks
Depeng Xu, Shuhan Yuan, Lu Zhang, and Xintao Wu · 2018
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One-network adversarial fairness
Tameem Adel, Isabel Valera, Zoubin Ghahramani, and Adrian Weller · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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50 years of test (un) fairness: Lessons for machine learning
Ben Hutchinson and Margaret Mitchell · 2019
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Adafair: Cumulative fairness adaptive boosting
Vasileios Iosifidis and Eirini Ntoutsi · 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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Fairness gan: Generating datasets with fairness properties using a generative adversarial network
Prasanna Sattigeri, Samuel C Hoffman, Vijil Chenthamarakshan, and Kush R Varshney · 2019
Diffusion models as plug-and-play priors
Alexandros Graikos, Nikolay Malkin, Nebojsa Jojic, and Dimitris Samaras · 2022
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Learning to re-weight examples with optimal transport for imbalanced classification
Dandan Guo, Zhuo Li, He Zhao, Mingyuan Zhou, Hongyuan Zha, et al · 2022
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A systematic study of bias amplification
Melissa Hall, Laurens van der Maaten, Laura Gustafson, Maxwell Jones, and Aaron Adcock · 2022
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Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
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Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation
Dongjun Kim, Seungjae Shin, Kyungwoo Song, Wanmo Kang, and Il-Chul Moon · 2022
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Learning controllable fair representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Ncp-vae: Variational autoencoders with noise contrastive priors
Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat · 2020
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Fair generative modeling via weak supervision
Kristy Choi, Aditya Grover, Trisha Singh, Rui Shu, and Stefano Ermon · 2020
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Fair generation through prior modification
Eric Frankel and Edward Vendrow · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Unsupervised controllable generation with score-based diffusion models: Disentangled latent code guidance
Yeongmin Kim, Dongjun Kim, HyeonMin Lee, and Il chul Moon · 2022
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Maximum likelihood training for score-based diffusion odes by high order denoising score matching
Cheng Lu, Kaiwen Zheng, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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The bias problem: Stable diffusion, 2022
Vittorio Maggio · 2022
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Fine-tuning diffusion models with limited data
Taehong Moon, Moonseok Choi, Gayoung Lee, Jung-Woo Ha, and Juho Lee · 2022
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob Mcgrew, Ilya Sutskever, and Mark Chen · 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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Adaptive multi-stage density ratio estimation for learning latent space energy-based model
Zhisheng Xiao and Tian Han · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2022
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Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations
Min Zhao, Fan Bao, Chongxuan Li, and Jun Zhu · 2022
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Fair diffusion: Instructing text-to-image generation models on fairness
Felix Friedrich, Patrick Schramowski, Manuel Brack, Lukas Struppek, Dominik Hintersdorf, Sasha Luccioni, and Kristian Kersting · 2023
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Adaptive priority reweighing for generalizing fairness improvement
Zhihao Hu, Yiran Xu, and Xinmei Tian · 2023
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Refining generative process with discriminator guidance in score-based diffusion models
Dongjun Kim, Yeongmin Kim, Se Jung Kwon, Wanmo Kang, and Il-Chul Moon · 2023
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Fp-diffusion: Improving score-based diffusion models by enforcing the underlying score fokker-planck equation
Chieh-Hsin Lai, Yuhta Takida, Naoki Murata, Toshimitsu Uesaka, Yuki Mitsufuji, and Stefano Ermon · 2023
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman · 2023
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Loss-guided diffusion models for plug-and-play controllable generation
Jiaming Song, Qinsheng Zhang, Hongxu Yin, Morteza Mardani, Ming-Yu Liu, Jan Kautz, Yongxin Chen, and Arash Vahdat · 2023
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Fair generative models via transfer learning
Christopher TH Teo, Milad Abdollahzadeh, and Ngai-Man Cheung · 2023
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A fair generative model using lecam divergence
Soobin Um and Changho Suh · 2023
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Stable target field for reduced variance score estimation in diffusion models
Yilun Xu, Shangyuan Tong, and Tommi S. Jaakkola · 2023
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Label-noise robust diffusion models
Byeonghu Na, Yeongmin Kim, HeeSun Bae, Jung Hyun Lee, Se Jung Kwon, Wanmo Kang, and Il chul Moon · 2024
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Denoising diffusion bridge models
Linqi Zhou, Aaron Lou, Samar Khanna, and Stefano Ermon · 2024
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