Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network

22 Jul 2016Filip Piekniewski • Patryk Laurent • Csaba Petre • Micah Richert • Dimitry Fisher • Todd Hylton

Understanding visual reality involves acquiring common-sense knowledge about countless regularities in the visual world, e.g., how illumination alters the appearance of objects in a scene, and how motion changes their apparent spatial relationship. These regularities are hard to label for training supervised machine learning algorithms; consequently, algorithms need to learn these regularities from the real world in an unsupervised way. The results suggest a new class of AI algorithms that uniquely combine prediction and scalability in a way that makes them suitable for learning from and --- and eventually acting within --- the real world.

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