The PredNet is a deep convolutional recurrent neural network inspired by the principles of predictive coding from the neuroscience literature [1, 2]. It is trained for next-frame video prediction with the belief that prediction is an effective objective for unsupervised (or "self-supervised") learning [e.g. 3-11]. The PredNet architecture is illustrated below. An animation of the flow of information in the network can be found here.
Next frame predictions on the Caltech Pedestrian  dataset are shown below. The model was trained on the KITTI dataset . See the repo for downloading the model.
Multi-timestep ahead predictions can be made by recursively feeding predictions back into the model. Below are several examples for a PredNet model fine-tuned for this task.
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- R. B. Palm. Prediction as a candidate for learning deep hierarchical models of data. Master’s thesis, Technical University of Denmark, 2012.
- R. C. O’Reilly, D. Wyatte, and J. Rohrlich. Learning through time in the thalamocortical loops. arXiv, 2014.
- N. Srivastava, E. Mansimov, and R. Salakhutdinov. Unsupervised learning of video representations using lstms. arXiv, 2015.
- R. Goroshin, M. Mathieu, and Y. LeCun. Learning to linearize under uncertainty. arXiv, 2015.
- M. Mathieu, C. Couprie, and Y. LeCun. Deep multi-scale video prediction beyond mean square error. arXiv, 2015.
- W. Lotter, G. Kreiman, and D. Cox. Unsupervised learning of visual structure using predictive generative networks. arXiv, 2015.
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- P. Dollár, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: A benchmark. CVPR, 2009.
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