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Diffusion models have shown remarkable performance on many generative tasks.
Differential equations, dynamical systems, and linear algebra , volume 60
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Stochastic backpropagation and approximate inference in deep generative models
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Deep unsupervised learning using nonequilibrium thermodynamics
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Pixel recurrent neural networks
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Good semi-supervised learning that requires a bad gan
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
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Progressive growing of gans for improved quality, stability, and variation
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Pixeldefend: Leveraging generative models to understand and defend against adversarial examples
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Neural ordinary differential equations
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Generative adversarial networks: An overview
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Scalable reversible generative models with free-form continuous dynamics
Grathwohl, W., Chen, R. T. Q., Bettencourt, J., and Duvenaud, D · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Likelihood training of schr \ \backslash ” odinger bridge using forward-backward sdes theory
Chen, T., Liu, G.-H., and Theodorou, E. A · 2021
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Diffusion schrödinger bridge with applications to score-based generative modeling
De Bortoli, V., Thornton, J., Heng, J., and Doucet, A · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J · 2021
Action matching: A variational method for learning stochastic dynamics from samples
Neklyudov, K., Severo, D., and Makhzani, A · 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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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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Deep generative modeling
Tomczak, J. M · 2022
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2022
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Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
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Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B · 2021
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Grad-tts: A diffusion probabilistic model for text-to-speech
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Image super-resolution via iterative refinement
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Tachibana, H., Go, M., Inahara, M., Katayama, Y., and Watanabe, Y · 2021
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Score-based generative modeling in latent space
Vahdat, A., Kreis, K., and Kautz, J · 2021
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Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Shao, Y., Zhang, W., Cui, B., and Yang, M.-H · 2022
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Building normalizing flows with stochastic interpolants
Albergo, M. S. and Vanden-Eijnden, E · 2023
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f-DM: A multi-stage diffusion model via progressive signal transformation
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Blurring diffusion models
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Minimizing trajectory curvature of ode-based generative models
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Flow matching for generative modeling
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Flow straight and fast: Learning to generate and transfer data with rectified flow
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Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem
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Diffenc: Variational diffusion with a learned encoder
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