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Diffusion-based generative models have emerged as powerful tools in the realm of generative modeling.
Towards practical plug-and-play diffusion models
Hyojun Go, Yunsung Lee, Jin-Young Kim, Seunghyun Lee, Myeongho Jeong, Hyun Seung Lee, and Seungtaek Choi · 1971
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Statistics of natural images: Scaling in the woods
Daniel Ruderman and William Bialek · 1993
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The statistics of natural images
Daniel L Ruderman · 1994
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Introduction to numerical continuation methods
Eugene L Allgower and Kurt Georg · 2003
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Monte Carlo methods in financial engineering , volume 53
Paul Glasserman · 2004
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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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
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Self-paced learning for latent variable models
M Kumar, Benjamin Packer, and Daphne Koller · 2010
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From baby steps to leapfrog: How “less is more” in unsupervised dependency parsing
Valentin I Spitkovsky, Hiyan Alshawi, and Dan Jurafsky · 2010
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A general method for debiasing a monte carlo estimator
Don McLeish · 2011
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Self-paced learning with diversity
Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, and Alexander Hauptmann · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Curriculum learning of multiple tasks
Anastasia Pentina, Viktoriia Sharmanska, and Christoph H Lampert · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 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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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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On the power of curriculum learning in training deep networks
Guy Hacohen and Daphna Weinshall · 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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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Curriculum pre-training for end-to-end speech translation
Chengyi Wang, Yu Wu, Shujie Liu, Ming Zhou, and Zhenglu Yang · 2020
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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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu · 2022
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Cited alongside, same era.
Xiaoxia Wu, Ethan Dyer, and Behnam Neyshabur · 2020
Cited alongside, same era.
Does the order of training samples matter? improving neural data-to-text generation with curriculum learning
Ernie Chang, Hui-Syuan Yeh, and Vera Demberg · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
Score-based generative modeling with critically-damped langevin diffusion
Tim Dockhorn, Arash Vahdat, and Karsten Kreis · 2021
Cited alongside, same era.
Efficiently identifying task groupings for multi-task learning
Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, and Chelsea Finn · 2021
Cited alongside, same era.
Adaptive curriculum learning
Yajing Kong, Liu Liu, Jun Wang, and Dacheng Tao · 2021
Cited alongside, same era.
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Efficient diffusion training via min-snr weighting strategy
Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao, Dong Chen, Han Hu, Xin Geng, and Baining Guo · 2023
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Analyzing and improving the training dynamics of diffusion models
Tero Karras, Miika Aittala, Jaakko Lehtinen, Janne Hellsten, Timo Aila, and Samuli Laine · 2023
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Understanding diffusion objectives as the elbo with simple data augmentation
Diederik Kingma and Ruiqi Gao · 2023
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Multi-architecture multi-expert diffusion models
Yunsung Lee, Jin-Young Kim, Hyojun Go, Myeongho Jeong, Shinhyeok Oh, and Seungtaek Choi · 2023
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Autodiffusion: Training-free optimization of time steps and architectures for automated diffusion model acceleration
Lijiang Li, Huixia Li, Xiawu Zheng, Jie Wu, Xuefeng Xiao, Rui Wang, Min Zheng, Xin Pan, Fei Chao, and Rongrong Ji · 2023
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Improved techniques for training consistency models
Yang Song and Prafulla Dhariwal · 2023
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Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever · 2023
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Any-to-any generation via composable diffusion
Zineng Tang, Ziyi Yang, Chenguang Zhu, Michael Zeng, and Mohit Bansal · 2023
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Harmonyview: Harmonizing consistency and diversity in one-image-to-3d
Sangmin Woo, Byeongjun Park, Hyojun Go, Jin-Young Kim, and Changick Kim · 2023
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Stable target field for reduced variance score estimation in diffusion models
Yilun Xu, Shangyuan Tong, and Tommi Jaakkola · 2023
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Guiding a diffusion model with a bad version of itself
Tero Karras, Miika Aittala, Tuomas Kynkäänniemi, Jaakko Lehtinen, Timo Aila, and Samuli Laine · 2024
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Sit: Exploring flow and diffusion-based generative models with scalable interpolant transformers
Nanye Ma, Mark Goldstein, Michael S Albergo, Nicholas M Boffi, Eric Vanden-Eijnden, and Saining Xie · 2024
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T-stitch: Accelerating sampling in pre-trained diffusion models with trajectory stitching
Zizheng Pan, Bohan Zhuang, De-An Huang, Weili Nie, Zhiding Yu, Chaowei Xiao, Jianfei Cai, and Anima Anandkumar · 2024
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Exploring diffusion time-steps for unsupervised representation learning
Zhongqi Yue, Jiankun Wang, Qianru Sun, Lei Ji, Eric I-Chao Chang, and Hanwang Zhang · 2024
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