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We obtain an analytic, interpretable and predictive theory of creativity in convolutional diffusion models.
Texture synthesis by non-parametric sampling
Efros, A. A. and Leung, T. K · 1999
Earlier work this paper cites.
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
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Group equivariant convolutional networks
Cohen, T. and Welling, M · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
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Convergence of denoising diffusion models under the manifold hypothesis
De Bortoli, V · 2022
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Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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Convergence for score-based generative modeling with polynomial complexity
Lee, H., Lu, J., and Tan, Y · 2022
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Diffusion-lm improves controllable text generation
Li, X., Thickstun, J., Gulrajani, I., Liang, P. S., and Hashimoto, T. B · 2022
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Flow matching for generative modeling
Lipman, Y., Chen, R. T., Ben-Hamu, H., Nickel, M., and Le, M · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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In search of dispersed memories: Generative diffusion models are associative memory networks
Ambrogioni, L · 2023
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Stable video diffusion: Scaling latent video diffusion models to large datasets
Blattmann, A., Dockhorn, T., Kulal, S., Mendelevitch, D., Kilian, M., Lorenz, D., Levi, Y., English, Z., Voleti, V., Letts, A., et al · 2023
Cited alongside, same era.
Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data
The emergence of reproducibility and consistency in diffusion models
Zhang, H., Zhou, J., Lu, Y., Guo, M., Wang, P., Shen, L., and Qu, Q · 2023
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Diffusion models in de novo drug design
Alakhdar, A., Poczos, B., and Washburn, N · 2024
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Nearly d-linear convergence bounds for diffusion models via stochastic localization
Benton, J., Bortoli, V., Doucet, A., and Deligiannidis, G · 2024
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Cifake: Image classification and explainable identification of ai-generated synthetic images
Bird, J. J. and Lotfi, A · 2024
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Dynamical regimes of diffusion models
Biroli, G., Bonnaire, T., De Bortoli, V., and Mézard, M · 2024
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Chen, M., Huang, K., Zhao, T., and Wang, M · 2023
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High-dimensional asymptotics of denoising autoencoders
Cui, H. and Zdeborová, L · 2023
Cited alongside, same era.
Analysis of learning a flow-based generative model from limited sample complexity
Cui, H., Krzakala, F., Vanden-Eijnden, E., and Zdeborová, L · 2023
Cited alongside, same era.
On memorization in diffusion models
Gu, X., Du, C., Pang, T., Li, C., Lin, M., and Wang, Y · 2023
Cited alongside, same era.
Hoover, B., Strobelt, H., Krotov, D., Hoffman, J., Kira, Z., and Chau, D. H · 2023
Cited alongside, same era.
Diffusion models for non-autoregressive text generation: A survey
Li, Y., Zhou, K., Zhao, W. X., and Wen, J.-R · 2023
Cited alongside, same era.
Diffusion models are minimax optimal distribution estimators
Oko, K., Akiyama, S., and Suzuki, T · 2023
Cited alongside, same era.
Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2023
Cited alongside, same era.
Lin, L., Gupta, N., Zhang, Y., Ren, H., Liu, C.-H., Ding, F., Wang, X., Li, X., Verdoliva, L., and Hu, S · 2024
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Towards a mechanistic explanation of diffusion model generalization
Niedoba, M., Zwartsenberg, B., Murphy, K., and Wood, F · 2024
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Compositional abilities emerge multiplicatively: Exploring diffusion models on a synthetic task
Okawa, M., Lubana, E. S., Dick, R., and Tanaka, H · 2024
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Memorization to generalization: The emergence of diffusion models from associative memory
Pham, B., Raya, G., Negri, M., Zaki, M. J., Ambrogioni, L., and Krotov, D · 2024
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A phase transition in diffusion models reveals the hierarchical nature of data
Sclocchi, A., Favero, A., and Wyart, M · 2024
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Minimal implementation of diffusion models
Sehwag, V · 2024
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Rethinking the spatial inconsistency in classifier-free diffusion guidance
Shen, D., Song, G., Xue, Z., Wang, F.-Y., and Liu, Y · 2024
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Manifolds, random matrices and spectral gaps: The geometric phases of generative diffusion
Ventura, E., Achilli, B., Silvestri, G., Lucibello, C., and Ambrogioni, L · 2024
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The unreasonable effectiveness of gaussian score approximation for diffusion models and its applications
Wang, B. and Vastola, J · 2024
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Diffusion models learn low-dimensional distributions via subspace clustering
Wang, P., Zhang, H., Zhang, Z., Chen, S., Ma, Y., and Qu, Q · 2024
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