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Generative models can be categorized into two types: explicit generative models that define explicit density forms and allow exact likelihood inference, such as score-based diffusion models (SDMs) and normalizing flows; implicit generative models that directly learn a transformation from the prior to the data distribution, such as generative adversarial nets (GANs).
Diffusions hypercontractives
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Brownian motion and stochastic calculus
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Stochastic differential equations
Øksendal, B. and Øksendal, B. (2003) · 2003
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Estimation of non-normalized statistical models by score matching
Hyvärinen, A. and Dayan, P. (2005) · 2005
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Concentration of measure and logarithmic sobolev inequalities
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A note on talagrand’s transportation inequality and logarithmic sobolev inequality
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Stochastic differential equations: an introduction with applications
Oksendal, B. (2013) · 2013
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Variational inference with normalizing flows
Rezende, D. and Mohamed, S. (2015) · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S. (2015) · 2015
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f-gan: Training generative neural samplers using variational divergence minimization
Nowozin, S., Cseke, B., and Tomioka, R. (2016) · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016) · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L. (2017) · 2017
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Improved training of wasserstein gans
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C. (2017) · 2017
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Sampling can be faster than optimization
Ma, Y.-A., Chen, Y., Jin, C., Flammarion, N., and Jordan, M. I. (2019) · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S. (2019) · 2019
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A. (2021) · 2021
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A variational perspective on diffusion-based generative models and score matching
Huang, C.-W., Lim, J. H., and Courville, A. C. (2021) · 2021
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Variational diffusion models
Kingma, D., Salimans, T., Poole, B., and Ho, J. (2021) · 2021
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Maximum likelihood training of score-based diffusion models
Song, Y., Durkan, C., Murray, I., and Ermon, S. (2021) · 2021
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Uniform poincaré and logarithmic sobolev inequalities for mean field particle systems
Guillin, A., Liu, W., Wu, L., and Zhang, C. (2022) · 2022
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Elucidating the design space of diffusion-based generative models
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Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2020) · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P. (2020) · 2020
Cited alongside, same era.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S. (2020) · 2020
Cited alongside, same era.
Denoising diffusion implicit models
Song, J., Meng, C., and Ermon, S. (2020a)
Cited in the paper.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B. (2020b)
Cited in the paper.
Karras, T., Aittala, M., Aila, T., and Laine, S. (2022) · 2022
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Efficient diffusion training via min-snr weighting strategy
Hang, T., Gu, S., Li, C., Bao, J., Chen, D., Hu, H., Geng, X., and Guo, B. (2023) · 2023
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Monoflow: Rethinking divergence gans via the perspective of differential equations
Yi, M., Zhu, Z., and Liu, S. (2023) · 2023
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Deep generative learning via variational gradient flow
Gao, Y., Jiao, Y., Wang, Y., Wang, Y., Yang, C., and Zhang, S. (2019) · 2093
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