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We present PredRNN++, an improved recurrent network for video predictive learning.
Learning representations by back-propagating errors
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Backpropagation through time: what it does and how to do it
Werbos, P. J · 1990
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Learning long-term dependencies with gradient descent is difficult
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Gradient-based learning algorithms for recurrent networks and their computational complexity
Williams, R. J. and Zipser, D · 1995
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Recognizing human actions: a local svm approach
Schuldt, C., Laptev, I., and Caputo, B · 2004
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
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On the difficulty of training recurrent neural networks
Pascanu, R., Mikolov, T., and Bengio, Y · 2013
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On the complexity of neural network classifiers: A comparison between shallow and deep architectures
Bianchini, M. and Scarselli, F · 2014
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Generative adversarial networks
Goodfellow, I. J., Pougetabadie, J., Mirza, M., Xu, B., Wardefarley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Video (language) modeling: a baseline for generative models of natural videos
Ranzato, M., Szlam, A., Bruna, J., Mathieu, M., Collobert, R., and Chopra, S · 2014
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
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Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E. L., Chintala, S., Fergus, R., et al · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Action-conditional video prediction using deep networks in atari games
Oh, J., Guo, X., Lee, H., Lewis, R. L., and Singh, S · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al · 2016
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Dynamic filter networks
De Brabandere, B., Jia, X., Tuytelaars, T., and Van Gool, L · 2016
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Unsupervised learning for physical interaction through video prediction
Unsupervised learning of disentangled representations from video
Denton, E. L. et al · 2017
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Video pixel networks
Kalchbrenner, N., Oord, A. v. d., Simonyan, K., Danihelka, I., Vinyals, O., Graves, A., and Kavukcuoglu, K · 2017
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Deep predictive coding networks for video prediction and unsupervised learning
Lotter, W., Kreiman, G., and Cox, D · 2017
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Flexible spatio-temporal networks for video prediction
Lu, C., Hirsch, M., and Schölkopf, B · 2017
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Deep learning for precipitation nowcasting: A benchmark and a new model
Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D.-Y., Wong, W.-k., and Woo, W.-c · 2017
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Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms
Wang, Y., Long, M., Wang, J., Gao, Z., and Philip, S. Y · 2017
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Finn, C., Goodfellow, I., and Levine, S · 2016
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Learning physical intuition of block towers by example
Lerer, A., Gross, S., and Fergus, R · 2016
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Deep multi-scale video prediction beyond mean square error
Mathieu, M., Couprie, C., and LeCun, Y · 2016
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Spatio-temporal video autoencoder with differentiable memory
Patraucean, V., Handa, A., and Cipolla, R · 2016
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Conditional image generation with pixelcnn decoders
van den Oord, A., Kalchbrenner, N., Espeholt, L., Vinyals, O., Graves, A., et al · 2016
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Generating videos with scene dynamics
Vondrick, C., Pirsiavash, H., and Torralba, A · 2016
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Temporal coherency based criteria for predicting video frames using deep multi-stage generative adversarial networks
Bhattacharjee, P. and Das, S · 2017
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Deep spatio-temporal residual networks for citywide crowd flows prediction
Zhang, J., Zheng, Y., and Qi, D · 2017
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Recurrent highway networks
Zilly, J. G., Srivastava, R. K., Koutník, J., and Schmidhuber, J · 2017
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Stochastic variational video prediction
Babaeizadeh, M., Finn, C., Erhan, D., Campbell, R. H., and Levine, S · 2018
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Stochastic video generation with a learned prior
Denton, E. and Fergus, R · 2018
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Mocogan: Decomposing motion and content for video generation
Tulyakov, S., Liu, M.-Y., Yang, X., and Kautz, J · 2018
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Predcnn: Predictive learning with cascade convolutions
Xu, Z., Wang, Y., Long, M., and Wang, J · 2018
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