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Diffusion models (DMs) have achieved state-of-the-art results for image synthesis tasks as well as density estimation.
An Introduction to Variational Autoencoders
Diederik P. Kingma and Max Welling · 1906
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A Simple Framework for Contrastive Learning of Visual Representations, June 2020
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Supervised Contrastive Learning, March 2021
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2004
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NVAE: A Deep Hierarchical Variational Autoencoder, January 2021
Arash Vahdat and Jan Kautz · 2007
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Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, and Ioannis Mitliagkas · 2009
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Denoising Diffusion Implicit Models
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Score-Based Generative Modeling through Stochastic Differential Equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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A Connection Between Score Matching and Denoising Autoencoders
Pascal Vincent · 2011
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High-Performance Neural Networks for Visual Object Classification, February 2011
Dan C. Cireşan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, and Jürgen Schmidhuber · 2011
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Taming Transformers for High-Resolution Image Synthesis
Patrick Esser, Robin Rombach, and Björn Ommer · 2012
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Auto-Encoding Variational Bayes, May 2014
Diederik P. Kingma and Max Welling · 2014
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Generative Adversarial Networks, June 2014
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Representation Learning: A Review and New Perspectives, April 2014
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2014
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics, November 2015
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep Residual Learning for Image Recognition, December 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation, May 2015
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2016
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Building machines that learn and think like people
Brenden M. Lake, Tomer D. Ullman, Joshua B. Tenenbaum, and Samuel J. Gershman · 2017
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ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2017
Diffusion Models Beat GANs on Image Synthesis
Prafulla Dhariwal and Alex Nichol · 2021
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Learning Transferable Visual Models From Natural Language Supervision, February 2021
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Diederik P. Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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High-Resolution Image Synthesis with Latent Diffusion Models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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β \beta -VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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Neural Discrete Representation Learning
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2018
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium, January 2018
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2018
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Progressive Growing of GANs for Improved Quality, Stability, and Variation, February 2018
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2018
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Brain Mechanisms of Concept Learning
Dagmar Zeithamova, Michael L. Mack, Kurt Braunlich, Tyler Davis, Carol A. Seger, Marlieke T.R. van Kesteren, and Andreas Wutz · 2019
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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Rätsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Konpat Preechakul, Nattanat Chatthee, Suttisak Wizadwongsa, and Supasorn Suwajanakorn · 2021
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Improved Denoising Diffusion Probabilistic Models
Alex Nichol and Prafulla Dhariwal · 2021
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Tackling the Generative Learning Trilemma with Denoising Diffusion GANs
Zhisheng Xiao, Karsten Kreis, and Arash Vahdat · 2021
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Score-based Generative Modeling in Latent Space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
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Diffusion-Based Representation Learning
Korbinian Abstreiter, Stefan Bauer, Bernhard Schölkopf, and Arash Mehrjou · 2021
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Symbolic Music Generation with Diffusion Models
Gautam Mittal, Jesse Engel, Curtis Hawthorne, and Ian Simon · 2021
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Hierarchical Text-Conditional Image Generation with CLIP Latents, April 2022
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Learning Disentangled Representations in the Imaging Domain, April 2022
Xiao Liu, Pedro Sanchez, Spyridon Thermos, Alison Q. O’Neil, and Sotirios A. Tsaftaris · 2022
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DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
Kushagra Pandey, Avideep Mukherjee, Piyush Rai, and Abhishek Kumar · 2022
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Perception Prioritized Training of Diffusion Models
Jooyoung Choi, Jungbeom Lee, Chaehun Shin, Sungwon Kim, Hyunwoo Kim, and Sungroh Yoon · 2022
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From Points to Functions: Infinite-dimensional Representations in Diffusion Models
Sarthak Mittal, Guillaume Lajoie, Stefan Bauer, and Arash Mehrjou · 2022
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