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Video Variational Autoencoder (VAE) encodes videos into a low-dimensional latent space, becoming a key component of most Latent Video Diffusion Models (LVDMs) to reduce model training costs.
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Alain Hore and Djemel Ziou · 2010
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Three-dimensional wavelet transform in multi-dimensional biomedical volume processing
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Zamir, and Mubarak Shah · 2012
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Auto-encoding variational bayes
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Huaibo Huang, Ran He, Zhenan Sun, and Tieniu Tan · 2017
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The kinetics human action video dataset, 2017
Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, Mustafa Suleyman, and Andrew Zisserman · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Temporal generative adversarial nets with singular value clipping
Masaki Saito, Eiichi Matsumoto, and Shunta Saito · 2017
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Learning to generate time-lapse videos using multi-stage dynamic generative adversarial networks
Wei Xiong, Wenhan Luo, Lin Ma, Wei Liu, and Jiebo Luo · 2017
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Group normalization
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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
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Fvd: A new metric for video generation
Thomas Unterthiner, Sjoerdvan Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly · 2019
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Frozen in time: A joint video and image encoder for end-to-end retrieval
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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