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Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
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The neural autoregressive distribution estimator
Hugo Larochelle and Iain Murray · 2011
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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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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Generating sentences from a continuous space
Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio · 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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Made: Masked autoencoder for distribution estimation
Mathieu Germain, Karol Gregor, Iain Murray, and Hugo Larochelle · 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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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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Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao · 2015
Cited alongside, same era.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Diederik P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Cited alongside, same era.
Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Pixelcnn++: A pixelcnn implementation with discretized logistic mixture likelihood and other modifications
Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P Kingma, and Yaroslav Bulatov · 2017
Later among the works it cites.
Convolutional normalizing flows
Guoqing Zheng, Yiming Yang, and Jaime G. Carbonell · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Łukasz Kaiser, Noam Shazeer, and Alexander Ku · 2018
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A note on the evaluation of generative models
L Theis, A van den Oord, and M Bethge · 2016
Cited alongside, same era.
Wavenet: A generative model for raw audio
Aäron Van Den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 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.
Pixelsnail: An improved autoregressive generative model
Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, and Pieter Abbeel · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
Cited alongside, same era.
Automatic differentiation in PyTorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Cited alongside, same era.
Parallel multiscale autoregressive density estimation
Scott Reed, Aäron Oord, Nal Kalchbrenner, Sergio Gómez Colmenarejo, Ziyu Wang, Yutian Chen, Dan Belov, and Nando Freitas · 2017
Cited alongside, same era.
Waveglow: A flow-based generative network for speech synthesis
Ryan Prenger, Rafael Valle, and Bryan Catanzaro · 2018
Later among the works it cites.
Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
Closest in time.
Flow++: Improving flow-based generative models with variational dequantization and architecture design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
Closest in time.
Emerging convolutions for generative normalizing flows
Emiel Hoogeboom, Rianne Van Den Berg, and Max Welling · 2019
Closest in time.
MAE: Mutual posterior-divergence regularization for variational autoencoders
Xuezhe Ma, Chunting Zhou, and Eduard Hovy · 2019
Closest in time.
Generating high fidelity images with subscale pixel networks and multidimensional upscaling, 2019
Jacob Menick and Nal Kalchbrenner · 2019
Closest in time.
Latent normalizing flows for discrete sequences
Zachary M Ziegler and Alexander M Rush · 2019
Closest in time.