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Normalizing flows, autoregressive models, variational autoencoders (VAEs), and deep energy-based models are among competing likelihood-based frameworks for deep generative learning.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Information maximization in noisy channels: A variational approach
David Barber and Felix V Agakov · 2004
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Learning multiple layers of features from tiny images, 2009
Alex Krizhevsky et al · 2009
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ImageNet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Training deep and recurrent networks with hessian-free optimization
James Martens and Ilya Sutskever · 2012
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Auto-encoding variational bayes
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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Learning visual representations at scale
Vincent Vanhoucke · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
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Draw: A recurrent neural network for image generation
Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra · 2015
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Importance weighted autoencoders
Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Fast and accurate deep network learning by exponential linear units (elus)
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
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Auxiliary deep generative models
Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther · 2016
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Hierarchical variational models
Rajesh Ranganath, Dustin Tran, and David Blei · 2016
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Rényi divergence variational inference
Yingzhen Li and Richard E Turner · 2016
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Bidirectional Helmholtz machines
Jorg Bornschein, Samira Shabanian, Asja Fischer, and Yoshua Bengio · 2016
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2016
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Categorical reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Discrete variational autoencoders
Jason Tyler Rolfe · 2016
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, 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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Pixel recurrent neural networks
Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Towards conceptual compression
Karol Gregor, Frederic Besse, Danilo Jimenez Rezende, Ivo Danihelka, and Daan Wierstra · 2016
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Ladder variational autoencoders
Casper Kaae Sønderby, Tapani Raiko, Lars Maaløe, Søren Kaae Sønderby, and Ole Winther · 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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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Training deep nets with sublinear memory cost
Tianqi Chen, Bing Xu, Chiyuan Zhang, and Carlos Guestrin · 2016
The challenge of realistic music generation: modelling raw audio at scale
Sander Dieleman, Aaron van den Oord, and Karen Simonyan · 2018
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PixelSNAIL: An improved autoregressive generative model
XI Chen, Nikhil Mishra, Mostafa Rohaninejad, and Pieter Abbeel · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Vae with a vampprior
Jakub Tomczak and Max Welling · 2018
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Cited alongside, same era.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 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
Cited alongside, same era.
Autoencoding beyond pixels using a learned similarity metric
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 2016
Cited alongside, same era.
Sticking the landing: Simple, lower-variance gradient estimators for variational inference
Geoffrey Roeder, Yuhuai Wu, and David K Duvenaud · 2017
Cited alongside, same era.
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, John Lawson, and Jascha Sohl-Dickstein · 2017
Cited alongside, same era.
Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
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The thermodynamic variational objective
Vaden Masrani, Tuan Anh Le, and Frank Wood · 2019
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Preventing posterior collapse with delta-vaes
Ali Razavi, Aäron van den Oord, Ben Poole, and Oriol Vinyals · 2019
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Don’t blame the elbo! a linear vae perspective on posterior collapse
James Lucas, George Tucker, Roger B Grosse, and Mohammad Norouzi · 2019
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Pixelvae++: Improved pixelvae with discrete prior
Hossein Sadeghi, Evgeny Andriyash, Walter Vinci, Lorenzo Buffoni, and Mohammad H Amin · 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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Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
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Learning hierarchical priors in vaes
Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, and Patrick van der Smagt · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Residual flows for invertible generative modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
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Flow++: Improving flow-based generative models with variational dequantization and architecture design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
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MAE: Mutual posterior-divergence regularization for variational autoencoders
Xuezhe Ma, Chunting Zhou, and Eduard Hovy · 2019
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MaCow: Masked convolutional generative flow
Xuezhe Ma, Xiang Kong, Shanghang Zhang, and Eduard Hovy · 2019
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Generating high fidelity images with subscale pixel networks and multidimensional upscaling
Jacob Menick and Nal Kalchbrenner · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Large scale GAN training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Bias correction of learned generative models using likelihood-free importance weighting
Aditya Grover, Jiaming Song, Ashish Kapoor, Kenneth Tran, Alekh Agarwal, Eric J Horvitz, and Stefano Ermon · 2019
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Undirected graphical models as approximate posteriors
Arash Vahdat, Evgeny Andriyash, and William G Macready · 2020
Closest in time.
Nvidia/apex, May 2020
Nvidia · 2020
Closest in time.
Vflow: More expressive generative flows with variational data augmentation
Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, and Tian Tian · 2020
Closest in time.
Augmented normalizing flows: Bridging the gap between generative flows and latent variable models
Chin-Wei Huang, Laurent Dinh, and Aaron Courville · 2020
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