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In the past, normalizing generative flows have emerged as a promising class of generative models for natural images.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2014
Earlier work this paper cites.
A note on the evaluation of generative models
Lucas Theis, Aäron van den Oord, and Matthias Bethge · 2015
Earlier work this paper cites.
Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
Earlier work this paper cites.
Improved variational inference with inverse autoregressive flow
Durk P Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, and Max Welling · 2016
Earlier work this paper cites.
PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2016
Earlier work this paper cites.
A downsampled variant of imagenet as an alternative to the cifar datasets
Patryk Chrabaszcz, Ilya Loshchilov, and Frank Hutter · 2017
Earlier work this paper cites.
Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Earlier work this paper cites.
PixelCNN models with auxiliary variables for natural image modeling
Alexander Kolesnikov and Christoph H Lampert · 2017
Earlier work this paper cites.
Fixing weight decay regularization in adam
Ilya Loshchilov, Frank Hutter, et al · 2017
Cited alongside, same era.
Masked autoregressive flow for density estimation
George Papamakarios, Theo Pavlakou, and Iain Murray · 2017
Cited alongside, same era.
Neural autoregressive flows
Chin-Wei Huang, David Krueger, Alexandre Lacoste, and Aaron Courville · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
Cited alongside, same era.
Flow++: Improving flow-based generative models with variational dequantization and architecture design
Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel · 2019
Generating high fidelity images with subscale pixel networks and multidimensional upscaling
Jacob Menick and Nal Kalchbrenner · 2019
Later among the works it cites.
Normalizing flows with multi-scale autoregressive priors
Apratim Bhattacharyya, Shweta Mahajan, Mario Fritz, Bernt Schiele, and Stefan Roth · 2020
Later among the works it cites.
Generative pretraining from pixels
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever · 2020
Later among the works it cites.
Big transfer (BiT): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Later among the works it cites.
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Cited alongside, same era.
Emerging convolutions for generative normalizing flows
Emiel Hoogeboom, Rianne Van Den Berg, and Max Welling · 2019
Cited alongside, same era.
GIVT: Generative infinite-vocabulary transformers
Michael Tschannen, Cian Eastwood, and Fabian Mentzer
Cited in the paper.
JetFormer: An autoregressive generative model of raw images and text
Michael Tschannen, André Susano Pinto, and Alexander Kolesnikov
Cited in the paper.
Conditional image generation with PixelCNN decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, and Alex Graves
Cited in the paper.
Pixel recurrent neural networks
Aäron Van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu
Cited in the paper.
Generative flows with invertible attentions
Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Radu Timofte, and Luc Van Gool · 2022
Later among the works it cites.
Normalizing flows are capable generative models
Shuangfei Zhai, Ruixiang Zhang, Preetum Nakkiran, David Berthelot, Jiatao Gu, Huangjie Zheng, Tianrong Chen, Miguel Angel Bautista, Navdeep Jaitly, and Josh Susskind · 2024
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