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Designed to learn long-range interactions on sequential data, transformers continue to show state-of-the-art results on a wide variety of tasks.
Learning the compositional nature of visual objects
B. Ommer and J. M. Buhmann · 2007
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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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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron C. Courville · 2013
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Generative Adversarial Nets
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2014
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Autoencoding beyond pixels using a learned similarity metric, 2015
Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, Hugo Larochelle, and Ole Winther · 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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Neural machine translation by jointly learning to align and translate, 2016
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2016
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Generating Images with Perceptual Similarity Metrics based on Deep Networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Discriminative regularization for generative models
Alex Lamb, Vincent Dumoulin, and Aaron C. Courville · 2016
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Deepfashion: Powering robust clothes recognition and retrieval with rich annotations
Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaoou Tang · 2016
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A decomposable attention model for natural language inference, 2016
Ankur P. Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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Pixel recurrent neural networks
Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Conditional image generation with pixelcnn decoders, 2016
Aaron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, and Koray Kavukcuoglu · 2016
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Semantic understanding of scenes through the ade20k dataset
Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba · 2016
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Photographic image synthesis with cascaded refinement networks
Qifeng Chen and Vladlen Koltun · 2017
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Image-to-Image Translation with Conditional Adversarial Networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros · 2017
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Progressive growing of gans for improved quality, stability, and variation
Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen · 2017
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Structured attention networks, 2017
Yoon Kim, Carl Denton, Luong Hoang, and Alexander M. Rush · 2017
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Attention is All you Need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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View synthesis by appearance flow, 2017
Tinghui Zhou, Shubham Tulsiani, Weilun Sun, Jitendra Malik, and Alexei A. Efros · 2017
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COCO-Stuff: Thing and stuff classes in context
Holger Caesar, Jasper Uijlings, and Vittorio Ferrari · 2018
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DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
Liang-Chieh Chen, G. Papandreou, I. Kokkinos, Kevin Murphy, and A. Yuille · 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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Pioneer networks: Progressively growing generative autoencoder
Ari Heljakka, Arno Solin, and Juho Kannala · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions
Diederik P. Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Generating wikipedia by summarizing long sequences
Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer · 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.
Improving language understanding by generative pre-training
A. Radford · 2018
Cited alongside, same era.
Neural discrete representation learning, 2018
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2018
Cited alongside, same era.
High-resolution image synthesis and semantic manipulation with conditional gans
Ting-Chun Wang, Ming-Yu Liu, Jun-Yan Zhu, Andrew Tao, Jan Kautz, and Bryan Catanzaro · 2018
Cited alongside, same era.
Very deep vaes generalize autoregressive models and can outperform them on images
Rewon Child · 2020
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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, et al · 2020
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A Disentangling Invertible Interpretation Network for Explaining Latent Representations
Patrick Esser, Robin Rombach, and Björn Ommer · 2020
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not-so-biggan: Generating high-fidelity images on a small compute budget
Seungwook Han, Akash Srivastava, Cole L. Hurwitz, Prasanna Sattigeri, and David D. Cox · 2020
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Towards photographic image manipulation with balanced growing of generative autoencoders
Ari Heljakka, Arno Solin, and Juho Kannala · 2020
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
Cited alongside, same era.
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2019
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Generating long sequences with sparse transformers, 2019
Rewon Child, Scott Gray, Alec Radford, and Ilya Sutskever · 2019
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Diagnosing and enhancing VAE models
Bin Dai and David P. Wipf · 2019
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Hierarchical autoregressive image models with auxiliary decoders
Jeffrey De Fauw, Sander Dieleman, and Karen Simonyan · 2019
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Denoising diffusion probabilistic models, 2020
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
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Analyzing and improving the image quality of stylegan
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High-fidelity generative image compression, 2020
Fabian Mentzer, George Toderici, Michael Tschannen, and Eirikur Agustsson · 2020
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High-fidelity performance metrics for generative models in pytorch, 2020
Anton Obukhov, Maximilian Seitzer, Po-Wei Wu, Semen Zhydenko, Jonathan Kyl, and Elvis Yu-Jing Lin · 2020
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Dual contradistinctive generative autoencoder, 2020
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Adversarial latent autoencoders
Stanislav Pidhorskyi, Donald A. Adjeroh, and Gianfranco Doretto · 2020
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
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Making sense of cnns: Interpreting deep representations and their invariances with inns
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Network-to-network translation with conditional invertible neural networks
Robin Rombach, Patrick Esser, and Bjorn Ommer · 2020
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A u-net based discriminator for generative adversarial networks
Edgar Schönfeld, Bernt Schiele, and Anna Khoreva · 2020
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Implementation of generating diverse high-fidelity images with vq-vae-2 in pytorch, 2020
Kim Seonghyeon · 2020
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NVAE: A deep hierarchical variational autoencoder
Arash Vahdat and Jan Kautz · 2020
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2020
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Cross-Domain Correspondence Learning for Exemplar-Based Image Translation
Pan Zhang, Bo Zhang, Dong Chen, Lu Yuan, and Fang Wen · 2020
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Diffusion models beat gans on image synthesis, 2021
Prafulla Dhariwal and Alex Nichol · 2021
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Improving augmentation and evaluation schemes for semantic image synthesis, 2021
Prateek Katiyar and Anna Khoreva · 2021
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Generating images with sparse representations, 2021
Charlie Nash, Jacob Menick, Sander Dieleman, and Peter W. Battaglia · 2021
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Improved denoising diffusion probabilistic models, 2021
Alex Nichol and Prafulla Dhariwal · 2021
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Zero-shot text-to-image generation, 2021
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
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Vaebm: A symbiosis between variational autoencoders and energy-based models, 2021
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