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Diffusion models are a state-of-the-art generative modeling framework that transform noise to images via Langevin sampling, guided by the score, which is the gradient of the logarithm of the data distribution.
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Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2017
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Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. C · 2017
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On convergence and stability of GANs
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MMD GAN: Towards deeper understanding of moment matching network
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Stein variational gradient descent as gradient flow
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Unbalanced Sobolev descent
Mroueh, Y. and Rigotti, M · 2020
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Non-saturating GAN training as divergence minimization
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
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Song, Y., Garg, S., Shi, J., and Ermon, S · 2020
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Deconstructing generative adversarial networks
Zhu, B., Jiao, J., and Tse, D · 2020
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Least squares generative adversarial networks
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., and Smolley, S. P · 2017
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Dual discriminator generative adversarial nets
Nguyen, T., Le, T., Vu, H., and Phung, D · 2017
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Masked autoregressive flow for density estimation
Papamakarios, G., Pavlakou, T., and Murray, I · 2017
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Banach Wasserstein GAN
Adler, J. and Lunz, S · 2018
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Demystifying MMD GANs
Bińkowski, M., Sutherland, D. J., Arbel, M., and Gretton, A · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Ansari, A. F., Ang, M. L., and Soh, H · 2021
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KALE flow: A relaxed KL gradient flow for probabilities with disjoint support
Glaser, P., Arbel, M., and Gretton, A · 2021
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Interpreting diffusion score matching using normalizing flow
Gong, W. and Li, Y · 2021
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Gotta go fast with score-based generative models
Jolicoeur-Martineau, A., Li, K., Piché-Taillefer, R., Kachman, T., and Mitliagkas, I · 2021
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Alias-free generative adversarial networks
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How well generative adversarial networks learn distributions
Liang, T · 2021
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On the convergence of gradient descent in GANs: MMD GAN as a gradient flow
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Normalizing flows for probabilistic modeling and inference
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Wasserstein GANs work because they fail (to approximate the Wasserstein distance)
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Kantorovich strikes back! Wasserstein GANs are not optimal transport?
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Score-based generative modeling secretly minimizes the Wasserstein distance
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High-resolution image synthesis with latent diffusion models
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StyleGAN-XL: scaling StyleGAN to large diverse datasets
Sauer, A., Schwarz, K., and Geiger, A · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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Scaling autoregressive models for content-rich text-to-image generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., Hutchinson, B., Han, W., Parekh, A., Li, X., Zhang, H., Baldridge, J., and Wu, Y · 2022
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Scaling up GANs for text-to-image synthesis
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Generative modelling with inverse heat dissipation
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