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Diffusion models have recently driven significant breakthroughs in generative modeling.
Bayesian learning for neural networks
Radford M Neal · 1995
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Laplace approximation of high dimensional integrals
Zhenming Shun and Peter McCullagh · 1995
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 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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Auto-encoding variational bayes
Diederik P Kingma, Max Welling, et al · 2014
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Weight uncertainty in neural network
Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 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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Uncertainty in deep learning
Yarin Gal et al · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2016
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 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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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Stochastic gradient descent as approximate bayesian inference
Stephan Mandt, Matthew D Hoffman, David M Blei, et al · 2017
Earlier work this paper cites.
Bayesian gan
Yunus Saatci and Andrew G Wilson · 2017
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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A scalable laplace approximation for neural networks
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
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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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Advances in variational inference
Cheng Zhang, Judith Bütepage, Hedvig Kjellström, and Stephan Mandt · 2018
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Bayesian variational autoencoders for unsupervised out-of-distribution detection
Erik Daxberger and José Miguel Hernández-Lobato · 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
Earlier work this paper cites.
A simple baseline for bayesian uncertainty in deep learning
Wesley J Maddox, Pavel Izmailov, Timur Garipov, Dmitry P Vetrov, and Andrew Gordon Wilson · 2019
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Do deep generative models know what they don’t know?
Eric Nalisnick, Akihiro Matsukawa, Yee Whye Teh, Dilan Gorur, and Balaji Lakshminarayanan · 2019
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Depth uncertainty in neural networks
Javier Antorán, James Allingham, and José Miguel Hernández-Lobato · 2020
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Giqa: Generated image quality assessment
Shuyang Gu, Jianmin Bao, Dong Chen, and Fang Wen · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Being bayesian, even just a bit, fixes overconfidence in relu networks
Agustinus Kristiadi, Matthias Hein, and Philipp Hennig · 2020
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Uncertainty estimation using a single deep deterministic neural network
Joost Van Amersfoort, Lewis Smith, Yee Whye Teh, and Yarin Gal · 2020
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Bayesian deep learning and a probabilistic perspective of generalization
Andrew G Wilson and Pavel Izmailov · 2020
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The case for bayesian deep learning
Andrew Gordon Wilson · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Diffusion models already have a semantic latent space
Mingi Kwon, Jaeseok Jeong, and Youngjung Uh · 2023
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Flow matching for generative modeling
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le · 2023
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2023
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Uncertainty and structure in neural ordinary differential equations
Katharina Ott, Michael Tiemann, and Philipp Hennig · 2023
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Scalable diffusion models with transformers
William Peebles and Saining Xie · 2023
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Do bayesian neural networks need to be fully stochastic?
Mrinank Sharma, Sebastian Farquhar, Eric Nalisnick, and Tom Rainforth · 2023
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Scalable marginal likelihood estimation for model selection in deep learning
Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Rätsch, and Khan Mohammad Emtiyaz · 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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Image-to-image regression with distribution-free uncertainty quantification and applications in imaging
Anastasios N Angelopoulos, Amit Pal Kohli, Stephen Bates, Michael Jordan, Jitendra Malik, Thayer Alshaabi, Srigokul Upadhyayula, and Yaniv Romano · 2022
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2022
Cited alongside, same era.
Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Vıctor Garcia Satorras, Clément Vignac, and Max Welling · 2022
Cited alongside, same era.
Hands-on bayesian neural networks—a tutorial for deep learning users
Laurent Valentin Jospin, Hamid Laga, Farid Boussaid, Wray Buntine, and Mohammed Bennamoun · 2022
Cited alongside, same era.
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How to trust your diffusion model: A convex optimization approach to conformal risk control
Jacopo Teneggi, Matthew Tivnan, Web Stayman, and Jeremias Sulam · 2023
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Fully bayesian autoencoders with latent sparse gaussian processes
Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, and Maurizio Filippone · 2023
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Understanding hallucinations in diffusion models through mode interpolation
Sumukh K Aithal, Pratyush Maini, Zachary Lipton, and J Zico Kolter · 2024
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Shedding light on large generative networks: Estimating epistemic uncertainty in diffusion models
Lucas Berry, Axel Brando, and David Meger · 2024
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Estimating epistemic and aleatoric uncertainty with a single model
Matthew Albert Chan, Maria J Molina, and Christopher Metzler · 2024
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Equivariant neural diffusion for molecule generation
François Cornet, Grigory Bartosh, Mikkel N Schmidt, and Christian A Naesseth · 2024
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Scaling rectified flow transformers for high-resolution image synthesis
Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, Dustin Podell, Tim Dockhorn, Zion English, and Robin Rombach · 2024
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Anomaly score: Evaluating generative models and individual generated images based on complexity and vulnerability
Jaehui Hwang, Junghyuk Lee, and Jong-Seok Lee · 2024
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Bayesdiff: Estimating pixel-wise uncertainty in diffusion via bayesian inference
Siqi Kou, Lei Gan, Dequan Wang, Chongxuan Li, and Zhijie Deng · 2024
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Absorb & escape: Overcoming single model limitations in generating heterogeneous genomic sequences
Zehui Li, Yuhao Ni, Guoxuan Xia, William Beardall, Akashaditya Das, Guy-Bart Stan, and Yiren Zhao · 2024
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Diffusion hyperfeatures: Searching through time and space for semantic correspondence
Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, and Trevor Darrell · 2024
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Emerdiff: Emerging pixel-level semantic knowledge in diffusion models
Koichi Namekata, Amirmojtaba Sabour, Sanja Fidler, and Seung Wook Kim · 2024
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The memory-perturbation equation: Understanding model’s sensitivity to data
Peter Nickl, Lu Xu, Dharmesh Tailor, Thomas Möllenhoff, and Mohammad Emtiyaz E Khan · 2024
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What makes an image realistic?
Lucas Theis · 2024
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Diffusion models in text generation: a survey
Qiuhua Yi, Xiangfan Chen, Chenwei Zhang, Zehai Zhou, Linan Zhu, and Xiangjie Kong · 2024
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A lightweight generalizable evaluation and enhancement framework for generative models and generated samples
Ganning Zhao, Vasileios Magoulianitis, Suya You, and C-C Jay Kuo · 2024
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Diffusion model guided sampling with pixel-wise aleatoric uncertainty estimation
Michele De Vita and Vasileios Belagiannis · 2025
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Towards understanding and quantifying uncertainty for text-to-image generation
Gianni Franchi, Nacim Belkhir, Dat Nguyen Trong, Guoxuan Xia, and Andrea Pilzer · 2025
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A-bench: Are lmms masters at evaluating ai-generated images?
Zicheng Zhang, Haoning Wu, Chunyi Li, Yingjie Zhou, Wei Sun, Xiongkuo Min, Zijian Chen, Xiaohong Liu, Weisi Lin, and Guangtao Zhai · 2025
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