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We present a hierarchical VAE that, for the first time, generates samples quickly while outperforming the PixelCNN in log-likelihood on all natural image benchmarks.
The helmholtz machine
Peter Dayan, Geoffrey E Hinton, Radford M Neal, and Richard S Zemel · 1995
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Rnade: The real-valued neural autoregressive density-estimator
Benigno Uria, Iain Murray, and Hugo Larochelle · 2013
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Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
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Stochastic gradient vb and the variational auto-encoder
Diederik P Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Pixelvae: A latent variable model for natural images
Ishaan Gulrajani, Kundan Kumar, Faruk Ahmed, Adrien Ali Taiga, Francesco Visin, David Vazquez, and Aaron Courville · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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Ladder variational autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 2016
Cited alongside, same era.
Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Pixelsnail: An improved autoregressive generative model
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, and Pieter Abbeel · 2017
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Learnable explicit density for continuous latent space and variational inference
Chin-Wei Huang, Ahmed Touati, Laurent Dinh, Michal Drozdzal, Mohammad Havaei, Laurent Charlin, and Aaron Courville · 2017
Cited alongside, same era.
Pixelcnn models with auxiliary variables for natural image modeling
Alexander Kolesnikov and Christoph H Lampert · 2017
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Diagnosing and enhancing vae models
Bin Dai and David Wipf · 2019
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Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
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An introduction to variational autoencoders
Diederik P Kingma and Max Welling · 2019
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Biva: A very deep hierarchy of latent variables for generative modeling
Lars Maaløe, Marco Fraccaro, Valentin Liévin, and Ole Winther · 2019
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Normalizing flows for probabilistic modeling and inference
George Papamakarios, Eric Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan · 2019
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Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Dan Belov, and Nando De Freitas · 2017
Cited alongside, same era.
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
Cited alongside, same era.
Learning hierarchical features from generative models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
Cited alongside, same era.
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Generating high fidelity images with subscale pixel networks and multidimensional upscaling
Jacob Menick and Nal Kalchbrenner · 2018
Cited alongside, same era.
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever · 2019
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Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
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Fixup initialization: Residual learning without normalization
Hongyi Zhang, Yann N Dauphin, and Tengyu Ma · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeff Wu, Heewoo Jun, Prafulla Dhariwal, David Luan, and Ilya Sutskever · 2020
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Jukebox: A generative model for music
Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, and Ilya Sutskever · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Nvae: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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