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Diffusion-based Deep Generative Models (DDGMs) offer state-of-the-art performance in generative modeling.
Multiscale structural similarity for image quality assessment
Z. Wang, E. P. Simoncelli, and A. C. Bovik · 2003
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Extracting and composing robust features with denoising autoencoders
P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol · 2008
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Improving neural networks by preventing co-adaptation of feature detectors
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov · 2012
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Generalized denoising auto-encoders as generative models
Y. Bengio, L. Yao, G. Alain, and P. Vincent · 2013
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What regularized auto-encoders learn from the data-generating distribution
G. Alain and Y. Bengio · 2014
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Marginalized denoising auto-encoders for nonlinear representations
M. Chen, K. Weinberger, F. Sha, and Y. Bengio · 2014
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Auto-Encoding Variational Bayes
D. P. Kingma and M. Welling · 2014
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2017
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms, 2017
H. Xiao, K. Rasul, and R. Vollgraf · 2017
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Diffusion models beat GANs on image synthesis
P. Dhariwal and A. Nichol · 2021
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A variational perspective on diffusion-based generative models and score matching
C.-W. Huang, J. H. Lim, and A. C. Courville · 2021
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Variational diffusion models
D. P. Kingma, T. Salimans, B. Poole, and J. Ho · 2021
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Generating images with sparse representations
C. Nash, J. Menick, S. Dieleman, and P. W. Battaglia · 2021
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Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
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Score-based generative modeling in latent space
A. Vahdat, K. Kreis, and J. Kautz · 2021
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M. S. Sajjadi, O. Bachem, M. Lucic, O. Bousquet, and S. Gelly · 2018
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Generative modeling by estimating gradients of the data distribution
Y. Song and S. Ermon · 2019
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Neural stochastic differential equations: Deep latent gaussian models in the diffusion limit
B. Tzen and M. Raginsky · 2019
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2020
Cited alongside, same era.
Diffusion priors in variational autoencoders
A. Wehenkel and G. Louppe · 2021
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Cascaded diffusion models for high fidelity image generation
J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans · 2022
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Progressive distillation for fast sampling of diffusion models
T. Salimans and J. Ho · 2022
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Deep Generative Modeling
J. M. Tomczak · 2022
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