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We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
On the anatomy of MCMC-based maximum likelihood learning of energy-based models
Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu · 1903
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Training generative adversarial networks with limited data
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The communication complexity of correlation
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A connection between score matching and denoising autoencoders
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Auto-encoding variational Bayes
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Generative adversarial nets
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Stochastic backpropagation and approximate inference in deep generative models
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Adam: A method for stochastic optimization
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Variational inference with normalizing flows
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U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Markov Chain Monte Carlo and variational inference: Bridging the gap
Tim Salimans, Diederik Kingma, and Max Welling · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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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
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GSNs: generative stochastic networks
Guillaume Alain, Yoshua Bengio, Li Yao, Jason Yosinski, Eric Thibodeau-Laufer, Saizheng Zhang, and Pascal Vincent · 2016
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 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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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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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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A theory of generative convnet
Jianwen Xie, Yang Lu, Song-Chun Zhu, and Yingnian Wu · 2016
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Sergey Zagoruyko and Nikos Komodakis · 2016
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Learning to generate samples from noise through infusion training
Florian Bordes, Sina Honari, and Pascal Vincent · 2017
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Variational walkback: Learning a transition operator as a stochastic recurrent net
Anirudh Goyal, Nan Rosemary Ke, Surya Ganguli, and Yoshua Bengio · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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beta-VAE: Learning basic visual concepts with a constrained variational framework
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Video pixel networks
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FFJORD: Free-form continuous dynamics for scalable reversible generative models
Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, and David Duvenaud · 2019
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Minimal random code learning: Getting bits back from compressed model parameters
Marton Havasi, Robert Peharz, and José Miguel Hernández-Lobato · 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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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Energy-inspired models: Learning with sampler-induced distributions
John Lawson, George Tucker, Bo Dai, and Rajesh Ranganath · 2019
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PixelCNN++: Improving the PixelCNN with discretized logistic mixture likelihood and other modifications
Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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A-NICE-MC: Adversarial training for MCMC
Jiaming Song, Shengjia Zhao, and Stefano Ermon · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Synthesizing dynamic patterns by spatial-temporal generative convnet
Jianwen Xie, Song-Chun Zhu, and Ying Nian Wu · 2017
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Learning generative ConvNets via multi-grid modeling and sampling
Ruiqi Gao, Yang Lu, Junpei Zhou, Song-Chun Zhu, and Ying Nian Wu · 2018
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Efficient neural audio synthesis
Nal Kalchbrenner, Erich Elsen, Karen Simonyan, Seb Noury, Norman Casagrande, Edward Lockhart, Florian Stimberg, Aaron van den Oord, Sander Dieleman, and Koray Kavukcuoglu · 2018
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Progressive growing of GANs for improved quality, stability, and variation
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Lars Maaløe, Marco Fraccaro, Valentin Liévin, and Ole Winther · 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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WaveGlow: A flow-based generative network for speech synthesis
Ryan Prenger, Rafael Valle, and Bryan Catanzaro · 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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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Learning energy-based spatial-temporal generative convnets for dynamic patterns
Jianwen Xie, Song-Chun Zhu, and Ying Nian Wu · 2019
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Your GAN is secretly an energy-based model and you should use discriminator driven latent sampling
Tong Che, Ruixiang Zhang, Jascha Sohl-Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio · 2020
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Residual energy-based models for text generation
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Flow contrastive estimation of energy-based models
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Your classifier is secretly an energy based model and you should treat it like one
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Evaluating lossy compression rates of deep generative models
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VQ-DRAW: A sequential discrete VAE
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Improved techniques for training score-based generative models
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Cnn-generated images are surprisingly easy to spot…for now
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Predictive sampling with forecasting autoregressive models
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