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Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images.
Adversarial video generation on complex datasets
Clark, A.; Donahue, J.; and Simonyan, K. 2019 · 1907
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Determining optical flow
Horn, B. K.; and Schunck, B. G. 1981 · 1981
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Two deterministic half-quadratic regularization algorithms for computed imaging
Charbonnier, P.; Blanc-Feraud, L.; Aubert, G.; and Barlaud, M. 1994 · 1994
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Score-based generative modeling through stochastic differential equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2020 · 2011
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UCF101: A dataset of 101 human actions classes from videos in the wild
Soomro, K.; Zamir, A. R.; and Shah, M. 2012 · 2012
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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Conditional generative adversarial nets
Mirza, M.; and Osindero, S. 2014 · 2014
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Made: Masked autoencoder for distribution estimation
Germain, M.; Gregor, K.; Murray, I.; and Larochelle, H. 2015 · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
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Unsupervised learning of video representations using lstms
Srivastava, N.; Mansimov, E.; and Salakhudinov, R. 2015 · 2015
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Learning spatiotemporal features with 3d convolutional networks
Tran, D.; Bourdev, L.; Fergus, R.; Torresani, L.; and Paluri, M. 2015 · 2015
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Density estimation using real nvp
Dinh, L.; Sohl-Dickstein, J.; and Bengio, S. 2016 · 2016
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A learned representation for artistic style
Dumoulin, V.; Shlens, J.; and Kudlur, M. 2016 · 2016
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Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
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Pixel recurrent neural networks
Van Oord, A.; Kalchbrenner, N.; and Kavukcuoglu, K. 2016 · 2016
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Generating videos with scene dynamics
Vondrick, C.; Pirsiavash, H.; and Torralba, A. 2016 · 2016
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Modulating early visual processing by language
De Vries, H.; Strub, F.; Mary, J.; Larochelle, H.; Pietquin, O.; and Courville, A. C. 2017 · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
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The kinetics human action video dataset
Kay, W.; Carreira, J.; Simonyan, K.; Zhang, B.; Hillier, C.; Vijayanarasimhan, S.; Viola, F.; Green, T.; Back, T.; Natsev, P.; et al. 2017 · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C.; Theis, L.; Huszár, F.; Caballero, J.; Cunningham, A.; Acosta, A.; Aitken, A.; Tejani, A.; Totz, J.; Wang, Z.; et al. 2017 · 2017
Cited alongside, same era.
Conditional image synthesis with auxiliary classifier gans
Odena, A.; Olah, C.; and Shlens, J. 2017 · 2017
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Optical flow estimation using a spatial pyramid network
Ranjan, A.; and Black, M. J. 2017 · 2017
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Temporal generative adversarial nets with singular value clipping
Saito, M.; Matsumoto, E.; and Saito, S. 2017 · 2017
Cited alongside, same era.
Salimans, T.; Karpathy, A.; Chen, X.; and Kingma, D. P. 2017 · 2017
Cited alongside, same era.
Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan
Saito, M.; Saito, S.; Koyama, M.; and Kobayashi, S. 2020 · 2020
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Implicit neural representations with periodic activation functions
Sitzmann, V.; Martel, J.; Bergman, A.; Lindell, D.; and Wetzstein, G. 2020 · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M.; Srinivasan, P.; Mildenhall, B.; Fridovich-Keil, S.; Raghavan, N.; Singhal, U.; Ramamoorthi, R.; Barron, J.; and Ng, R. 2020 · 2020
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Scaling Autoregressive Video Models
Weissenborn, D.; Täckström, O.; and Uszkoreit, J. 2020 · 2020
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Clevrer: Collision events for video representation and reasoning
Yi, K.; Gan, C.; Li, Y.; Kohli, P.; Wu, J.; Torralba, A.; and Tenenbaum, J. B. 2020 · 2020
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Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Towards high resolution video generation with progressive growing of sliced wasserstein gans
Acharya, D.; Huang, Z.; Paudel, D. P.; and Van Gool, L. 2018 · 2018
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Large scale GAN training for high fidelity natural image synthesis
Brock, A.; Donahue, J.; and Simonyan, K. 2018 · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P.; and Dhariwal, P. 2018 · 2018
Cited alongside, same era.
Which training methods for GANs do actually converge?
Mescheder, L.; Geiger, A.; and Nowozin, S. 2018 · 2018
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Spectral normalization for generative adversarial networks
Miyato, T.; Kataoka, T.; Koyama, M.; and Yoshida, Y. 2018 · 2018
Cited alongside, same era.
cGANs with projection discriminator
Miyato, T.; and Koyama, M. 2018 · 2018
Cited alongside, same era.
Ilvr: Conditioning method for denoising diffusion probabilistic models
Choi, J.; Kim, S.; Jeong, Y.; Gwon, Y.; and Yoon, S. 2021 · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
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Stylevideogan: A temporal generative model using a pretrained stylegan
Fox, G.; Tewari, A.; Elgharib, M.; and Theobalt, C. 2021 · 2021
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Ltt-gan: Looking through turbulence by inverting gans
Mei, K.; and Patel, V. M. 2021 · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q.; and Dhariwal, P. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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Zero-shot text-to-image generation
Ramesh, A.; Pavlov, M.; Goh, G.; Gray, S.; Voss, C.; Radford, A.; Chen, M.; and Sutskever, I. 2021 · 2021
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Adversarial generation of continuous images
Skorokhodov, I.; Ignatyev, S.; and Elhoseiny, M. 2021 · 2021
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Videogpt: Video generation using vq-vae and transformers
Yan, W.; Zhang, Y.; Abbeel, P.; and Srinivas, A. 2021 · 2021
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Long video generation with time-agnostic vqgan and time-sensitive transformer
Ge, S.; Hayes, T.; Yang, H.; Yin, X.; Pang, G.; Jacobs, D.; Huang, J.-B.; and Parikh, D. 2022 · 2022
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Flexible Diffusion Modeling of Long Videos
Harvey, W.; Naderiparizi, S.; Masrani, V.; Weilbach, C.; and Wood, F. 2022 · 2022
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AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models
Nair, N. G.; Mei, K.; and Patel, V. M. 2022 · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022
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StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2
Skorokhodov, I.; Tulyakov, S.; and Elhoseiny, M. 2022 · 2022
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Deblurring via Stochastic Refinement
Whang, J.; Delbracio, M.; Talebi, H.; Saharia, C.; Dimakis, A. G.; and Milanfar, P. 2022 · 2022
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Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
Yu, S.; Tack, J.; Mo, S.; Kim, H.; Kim, J.; Ha, J.-W.; and Shin, J. 2022 · 2022
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