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We explore the problem of generating minority samples using diffusion models.
An empirical bayes approach to statistics
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Lof: identifying density-based local outliers
Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander · 2000
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Calculus of variations
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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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 · 2011
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A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
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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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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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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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Inclusivefacenet: Improving face attribute detection with race and gender diversity
Hee Jung Ryu, Hartwig Adam, and Margaret Mitchell · 2017
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Deep image prior
Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Uncovering and mitigating algorithmic bias through learned latent structure
Alexander Amini, Ava P Soleimany, Wilko Schwarting, Sangeeta N Bhatia, and Daniela Rus · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 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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Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Generative modeling by estimating gradients of the data distribution
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Tackling the generative learning trilemma with denoising diffusion gans
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2021
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Rarity score: A new metric to evaluate the uncommonness of synthesized images
Jiyeon Han, Hwanil Choi, Yunjey Choi, Junho Kim, Jung-Woo Ha, and Jaesik Choi · 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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Refining generative process with discriminator guidance in score-based diffusion models
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Yang Song and Stefano Ermon · 2019
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Pyod: A python toolbox for scalable outlier detection
Yue Zhao, Zain Nasrullah, and Zheng Li · 2019
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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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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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Reliable fidelity and diversity metrics for generative models
Muhammad Ferjad Naeem, Seong Joon Oh, Youngjung Uh, Yunjey Choi, and Jaejun Yoo · 2020
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Inclusive gan: Improving data and minority coverage in generative models
Ning Yu, Ke Li, Peng Zhou, Jitendra Malik, Larry Davis, and Mario Fritz · 2020
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Dongjun Kim, Yeongmin Kim, Wanmo Kang, and Il-Chul Moon · 2022
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Exploring chemical space with score-based out-of-distribution generation
Seul Lee, Jaehyeong Jo, and Sung Ju Hwang · 2022
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Raregan: Generating samples for rare classes
Zinan Lin, Hao Liang, Giulia Fanti, Vyas Sekar, Rahul Anand Sharma, Elahe Soltanaghaei, Anthony Rowe, Hun Namkung, Zaoxing Liu, Daehyeok Kim, et al · 2022
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On aliased resizing and surprising subtleties in gan evaluation
Gaurav Parmar, Richard Zhang, and Jun-Yan Zhu · 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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Generating high fidelity data from low-density regions using diffusion models
Vikash Sehwag, Caner Hazirbas, Albert Gordo, Firat Ozgenel, and Cristian Canton · 2022
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Unsupervised visual defect detection with score-based generative model
Yapeng Teng, Haoyang Li, Fuzhen Cai, Ming Shao, and Siyu Xia · 2022
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Diffusion models for medical anomaly detection
Julia Wolleb, Florentin Bieder, Robin Sandkühler, and Philippe C Cattin · 2022
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Anoddpm: Anomaly detection with denoising diffusion probabilistic models using simplex noise
Julian Wyatt, Adam Leach, Sebastian M Schmon, and Chris G Willcocks · 2022
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Enhanced balancing gan: Minority-class image generation
Gaofeng Huang and Amir Hossein Jafari · 2023
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Class-balancing diffusion models
Yiming Qin, Huangjie Zheng, Jiangchao Yao, Mingyuan Zhou, and Ya Zhang · 2023
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It is all about where you start: Text-to-image generation with seed selection
Dvir Samuel, Rami Ben-Ari, Simon Raviv, Nir Darshan, and Gal Chechik · 2023
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