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Autoregressive models and their sequential factorization of the data likelihood have recently demonstrated great potential for image representation and synthesis.
Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 1912
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Über die analytischen methoden in der wahrscheinlichkeitsrechnung
A. Kolmogoroff · 1931
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A disentangling invertible interpretation network for explaining latent representations
P. Esser, R. Rombach, and B. Ommer · 2004
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, K. Li, and F. Li · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
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Taming transformers for high-resolution image synthesis
P. Esser, R. Rombach, and B. Ommer · 2012
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Generative adversarial networks
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio · 2014
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Auto-Encoding Variational Bayes
D. P. Kingma and M. Welling · 2014
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Semi-supervised learning with deep generative models
D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
D. J. Rezende, S. Mohamed, and D. Wierstra · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer · 2015
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MADE: masked autoencoder for distribution estimation
M. Germain, K. Gregor, I. Murray, and H. Larochelle · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. A. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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LSUN: construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao · 2015
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X. Chen, D. P. Kingma, T. Salimans, Y. Duan, P. Dhariwal, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Generating images with perceptual similarity metrics based on deep networks
A. Dosovitskiy and T. Brox · 2016
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Image style transfer using convolutional neural networks
L. A. Gatys, A. S. Ecker, and M. Bethge · 2016
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Professor forcing: A new algorithm for training recurrent networks
A. Goyal, A. Lamb, Y. Zhang, S. Zhang, A. C. Courville, and Y. Bengio · 2016
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Pixelvae: A latent variable model for natural images
I. Gulrajani, K. Kumar, F. Ahmed, A. A. Taïga, F. Visin, D. Vázquez, and A. C. Courville · 2016
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Perceptual losses for real-time style transfer and super-resolution
J. Johnson, A. Alahi, and L. Fei-Fei · 2016
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Disentangling factors of variation in deep representations using adversarial training
M. Mathieu, J. J. Zhao, P. Sprechmann, A. Ramesh, and Y. LeCun · 2016
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Sequence level training with recurrent neural networks
M. Ranzato, S. Chopra, M. Auli, and W. Zaremba · 2016
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Improved techniques for training gans
T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen · 2016
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Ladder variational autoencoders
C. K. Sønderby, T. Raiko, L. Maaløe, S. K. Sønderby, and O. Winther · 2016
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Neural autoregressive distribution estimation
B. Uria, M. Côté, K. Gregor, I. Murray, and H. Larochelle · 2016
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Sampling generative networks: Notes on a few effective techniques
T. White · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
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Semantic interpolation in implicit models
Y. Kilcher, A. Lucchi, and T. Hofmann · 2017
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Pixelcnn models with auxiliary variables for natural image modeling
A. Kolesnikov and C. H. Lampert · 2017
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SEARNN: training rnns with global-local losses
R. Leblond, J. Alayrac, A. Osokin, and S. Lacoste-Julien · 2017
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T. Salimans, A. Karpathy, X. Chen, and D. P. Kingma · 2017
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Language models are few-shot learners
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
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Generative pretraining from pixels
M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever · 2020
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Very deep vaes generalize autoregressive models and can outperform them on images
R. Child · 2020
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Jukebox: A generative model for music
P. Dhariwal, H. Jun, C. Payne, J. W. Kim, A. Radford, and I. Sutskever · 2020
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Musings on typicality, 2020
S. Dieleman · 2020
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Neural discrete representation learning
A. van den Oord, O. Vinyals, and K. Kavukcuoglu · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
BERT: pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2018
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Disentangling factors of variation with cycle-consistent variational auto-encoders
A. H. Jha, S. Anand, M. Singh, and V. S. R. Veeravasarapu · 2018
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Image transformer
N. Parmar, A. Vaswani, J. Uszkoreit, L. Kaiser, N. Shazeer, A. Ku, and D. Tran · 2018
Cited alongside, same era.
Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
P. Sharma, N. Ding, S. Goodman, and R. Soricut · 2018
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Challenges in disentangling independent factors of variation
A. Szabó, Q. Hu, T. Portenier, M. Zwicker, and P. Favaro · 2018
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer · 2020
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High-fidelity generative image compression
F. Mentzer, G. Toderici, M. Tschannen, and E. Agustsson · 2020
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Understanding guided image captioning performance across domains
E. G. Ng, B. Pang, P. Sharma, and R. Soricut · 2020
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Network-to-network translation with conditional invertible neural networks
R. Rombach, P. Esser, and B. Ommer · 2020
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2020
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NVAE: A deep hierarchical variational autoencoder
A. Vahdat and J. Kautz · 2020
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fairseq s2t: Fast speech-to-text modeling with fairseq, 2020
C. Wang, Y. Tang, X. Ma, A. Wu, D. Okhonko, and J. Pino · 2020
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Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows
R. Abdal, P. Zhu, N. J. Mitra, and P. Wonka · 2021
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Structured denoising diffusion models in discrete state-spaces
J. Austin, D. Johnson, J. Ho, D. Tarlow, and R. v. d. Berg · 2021
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Diffusion models beat gans on image synthesis
P. Dhariwal and A. Nichol · 2021
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Argmax flows and multinomial diffusion: Towards non-autoregressive language models
E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling · 2021
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Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation, 2021
J. Kasai, N. Pappas, H. Peng, J. Cross, and N. A. Smith · 2021
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Transformers in vision: A survey
S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah · 2021
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Generating images with sparse representations
C. Nash, J. Menick, S. Dieleman, and P. W. Battaglia · 2021
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Improved denoising diffusion probabilistic models
A. Nichol and P. Dhariwal · 2021
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Learning transferable visual models from natural language supervision
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever · 2021
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Zero-shot text-to-image generation
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever · 2021
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Videogpt: Video generation using VQ-VAE and transformers
W. Yan, Y. Zhang, P. Abbeel, and A. Srinivas · 2021
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Tfill: Image completion via a transformer-based architecture
C. Zheng, T. Cham, and J. Cai · 2021
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