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Ambient diffusion is a recently proposed framework for training diffusion models using corrupted data.
Estimation of the mean of a multivariate normal distribution
Stein, C. M · 1981
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Reverse-time diffusion equation models
Anderson, B. D · 1982
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Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S · 2010
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2011
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A · 2016
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Noise2noise: Learning image restoration without clean data
Lehtinen, J., Munkberg, J., Hasselgren, J., Laine, S., Karras, T., Aittala, M., and Aila, T · 2018
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Unsupervised learning with stein’s unbiased risk estimator
Metzler, C. A., Mousavi, A., Heckel, R., and Baraniuk, R. G · 2018
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Training deep learning based denoisers without ground truth data
Soltanayev, S. and Chun, S. Y · 2018
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Noise2self: Blind denoising by self-supervision
Batson, J. and Royer, L · 2019
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Noise2void-learning denoising from single noisy images
Krull, A., Buchholz, T.-O., and Jug, F · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Noisier2noise: Learning to denoise from unpaired noisy data
Moran, N., Schmidt, D., Zhong, Y., and Coady, P · 2020
Cited alongside, same era.
Noisy-as-clean: Learning self-supervised denoising from corrupted image
Xu, J., Huang, Y., Cheng, M.-M., Liu, L., Zhu, F., Xu, Z., and Shao, L · 2020
Cited alongside, same era.
Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising
Pang, T., Zheng, H., Quan, Y., and Ji, H · 2021
Cited alongside, same era.
Ensure: A general approach for unsupervised training of deep image reconstruction algorithms
Aggarwal, H. K., Pramanik, A., John, M., and Jacob, M · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Probability flow solution of the fokker–planck equation
Boffi, N. M. and Vanden-Eijnden, E · 2023
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Extracting training data from diffusion models
Carlini, N., Hayes, J., Nasr, M., Jagielski, M., Sehwag, V., Tramer, F., Balle, B., Ippolito, D., and Wallace, E · 2023
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Solving inverse problems with ambient diffusion
Daras, G. and Dimakis, A · 2023
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Datacomp: In search of the next generation of multimodal datasets
Gadre, S. Y., Ilharco, G., Fang, A., Hayase, J., Smyrnis, G., Nguyen, T., Marten, R., Wortsman, M., Ghosh, D., Zhang, J., et al · 2023
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Gsure-based diffusion model training with corrupted data
Kawar, B., Elata, N., Michaeli, T., and Elad, M · 2023
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Cited alongside, same era.
Laion-5b: An open large-scale dataset for training next generation image-text models
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al · 2022
Cited alongside, same era.
Self-consistency of the fokker planck equation
Shen, Z., Wang, Z., Kale, S., Ribeiro, A., Karbasi, A., and Hassani, H · 2022
Cited alongside, same era.
Diffusion art or digital forgery? investigating data replication in diffusion models
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., and Goldstein, T · 2022
Cited alongside, same era.
Solving inverse problems with score-based generative priors learned from noisy data
Aali, A., Arvinte, M., Kumar, S., and Tamir, J. I · 2023
Cited alongside, same era.
Stochastic interpolants: A unifying framework for flows and diffusions
Albergo, M. S., Boffi, N. M., and Vanden-Eijnden, E · 2023
Cited alongside, same era.
Improving image generation with better captions
Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al · 2023
Cited alongside, same era.
Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al · 2023
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Sdxl: Improving latent diffusion models for high-resolution image synthesis
Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., and Rombach, R · 2023
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Understanding and mitigating copying in diffusion models
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., and Goldstein, T · 2023
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Ddm 2 : Self-supervised diffusion mri denoising with generative diffusion models
Xiang, T., Yurt, M., Syed, A. B., Setsompop, K., and Chaudhari, A · 2023
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De Bortoli, V., Hutchinson, M., Wirnsberger, P., and Doucet, A · 2024
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Disguised copyright infringement of latent diffusion model
Lu, Y., Yang, M. Y., Liu, Z., Kamath, G., and Yu, Y · 2024
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First m87 event horizon telescope results. iv. imaging the central supermassive black hole
The Event Horizon Telescope Collaboration · 2041
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