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Video Pixel Networks
Nal Kalchbrenner · Karen Simonyan · Aäron van den Oord · Ivo Danihelka · Oriol Vinyals · Alex Graves · koray kavukcuoglu

Mon Aug 01:30 AM -- 05:00 AM PDT @ Gallery #18

We propose a probabilistic video model, the Video Pixel Network (VPN), that estimates the discrete joint distribution of the raw pixel values in a video. The model and the neural architecture reflect the time, space and color structure of video tensors and encode it as a four-dimensional dependency chain. The VPN approaches the best possible performance on the Moving MNIST benchmark, a leap over the previous state of the art, and the generated videos show only minor deviations from the ground truth. The VPN also produces detailed samples on the action-conditional Robotic Pushing benchmark and generalizes to the motion of novel objects.

Author Information

Nal Kalchbrenner (DeepMind)
Karen Simonyan (DeepMind)
Aäron van den Oord (Google)
Ivo Danihelka (Google DeepMind)
Oriol Vinyals (DeepMind)

Oriol Vinyals is a Research Scientist at Google. He works in deep learning with the Google Brain team. Oriol holds a Ph.D. in EECS from University of California, Berkeley, and a Masters degree from University of California, San Diego. He is a recipient of the 2011 Microsoft Research PhD Fellowship. He was an early adopter of the new deep learning wave at Berkeley, and in his thesis he focused on non-convex optimization and recurrent neural networks. At Google Brain he continues working on his areas of interest, which include artificial intelligence, with particular emphasis on machine learning, language, and vision.

Alex Graves (DeepMind)
koray kavukcuoglu (DeepMind)

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