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Being able to predict what may happen in the future requires an in-depth understanding of the physical and causal rules that govern the world.
Long short-term memory
Hochreiter, S., Schmidhuber, J.: · 1997
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: · 2004
Earlier work this paper cites.
Recognizing human actions: a local SVM approach
Schuldt, C., Laptev, I., Caputo, B.: · 2004
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Complex wavelet structural similarity: A new image similarity index
Sampat, M.P., Wang, Z., Gupta, S., Bovik, A.C., Markey, M.K.: · 2009
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D.P., Welling, M.: · 2014
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Earlier work this paper cites.
Video (language) modeling: a baseline for generative models of natural videos
Ranzato, M., Szlam, A., Bruna, J., Mathieu, M., Collobert, R., Chopra, S.: · 2014
Earlier work this paper cites.
Image database TID2013: Peculiarities, results and perspectives
Ponomarenko, N., Jin, L., Ieremeiev, O., Lukin, V., Egiazarian, K., Astola, J., Vozel, B., Chehdi, K., Carli, M., Battisti, F., et al.: · 2015
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Action-conditional video prediction using deep networks in atari games
Oh, J., Guo, X., Lee, H., Lewis, R.L., Singh, S.: · 2015
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Convolutional LSTM network: A machine learning approach for precipitation nowcasting
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.c.: · 2015
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Dense optical flow prediction from a static image
Walker, J., Gupta, A., Hebert, M.: · 2015
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Unsupervised learning of video representations using LSTMs
Srivastava, N., Mansimov, E., Salakhudinov, R.: · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 2015
Earlier work this paper cites.
Unsupervised learning for physical interaction through video prediction
Finn, C., Goodfellow, I., Levine, S.: · 2016
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Deep multi-scale video prediction beyond mean square error
Mathieu, M., Couprie, C., LeCun, Y.: · 2016
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Context encoders: Feature learning by inpainting
Pathak, D., Krähenbühl, P., Donahue, J., Darrell, T., Efros, A.: · 2016
Earlier work this paper cites.
Generating videos with scene dynamics
Vondrick, C., Pirsiavash, H., Torralba, A.: · 2016
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Autoencoding beyond pixels using a learned similarity metric
Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: · 2016
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Generating images with perceptual similarity metrics based on deep networks
Dosovitskiy, A., Brox, T.: · 2016
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Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., Fei-Fei, L.: · 2016
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Dynamic filter networks
De Brabandere, B., Jia, X., Tuytelaars, T., Van Gool, L.: · 2016
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Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks
Xue, T., Wu, J., Bouman, K., Freeman, B.: · 2016
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SE3-nets: Learning rigid body motion using deep neural networks
Byravan, A., Fox, D.: · 2016
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An uncertain future: Forecasting from static images using variational autoencoders
Walker, J., Doersch, C., Gupta, A., Hebert, M.: · 2016
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Pixel recurrent neural networks
van den Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: · 2016
Video imagination from a single image with transformation generation
Chen, B., Wang, W., Wang, J., Chen, X., Li, W.: · 2017
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Transformation-based models of video sequences
van Amersfoort, J., Kannan, A., Ranzato, M., Szlam, A., Tran, D., Chintala, S.: · 2017
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Video frame synthesis using deep voxel flow
Liu, Z., Yeh, R., Tang, X., Liu, Y., Agarwala, A.: · 2017
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Dual motion GAN for future-flow embedded video prediction
Liang, X., Lee, L., Dai, W., Xing, E.P.: · 2017
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Recurrent environment simulators
Chiappa, S., Racanière, S., Wierstra, D., Mohamed, S.: · 2017
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Video pixel networks
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Learning temporal transformations from time-lapse videos
Zhou, Y., Berg, T.L.: · 2016
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Colorful image colorization
Zhang, R., Isola, P., Efros, A.A.: · 2016
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NIPS 2016 tutorial: Generative adversarial networks
Goodfellow, I.: · 2016
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Instance normalization: The missing ingredient for fast stylization
Ulyanov, D., Vedaldi, A., Lempitsky, V.S.: · 2016
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Deconvolution and checkerboard artifacts
Odena, A., Dumoulin, V., Olah, C.: · 2016
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Generating sentences from a continuous space
Bowman, S., Vilnis, L., Vinyals, O., Dai, A.M., Jozefowicz, R., Bengio, S.: · 2016
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Kalchbrenner, N., van den Oord, A., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., Kavukcuoglu, K.: · 2017
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Parallel multiscale autoregressive density estimation
Reed, S.E., van den Oord, A., Kalchbrenner, N., Colmenarejo, S.G., Wang, Z., Chen, Y., Belov, D., de Freitas, N.: · 2017
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Temporal coherency based criteria for predicting video frames using deep multi-stage generative adversarial networks
Bhattacharjee, P., Das, S.: · 2017
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Deep predictive coding networks for video prediction and unsupervised learning
Lotter, W., Kreiman, G., Cox, D.: · 2017
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Unsupervised learning of disentangled representations from video
Denton, E., Birodkar, V.: · 2017
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Deep visual foresight for planning robot motion
Finn, C., Levine, S.: · 2017
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Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2017
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Video frame interpolation via adaptive separable convolution
Niklaus, S., Mai, L., Liu, F.: · 2017
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Stochastic variational video prediction
Babaeizadeh, M., Finn, C., Erhan, D., Campbell, R.H., Levine, S.: · 2018
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Stochastic video generation with a learned prior
Denton, E., Fergus, R.: · 2018
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MoCoGAN: Decomposing motion and content for video generation
Tulyakov, S., Liu, M.Y., Yang, X., Kautz, J.: · 2018
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The unreasonable effectiveness of deep networks as a perceptual metric
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: · 2018
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SE3-pose-nets: Structured deep dynamics models for visuomotor planning and control
Byravan, A., Leeb, F., Meier, F., Fox, D.: · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: · 2018
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