Unsupervised Learning from Continuous Video in a Scalable Predictive Recurrent Network
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. We present a novel network meta architecture that can learn world dynamics from raw, continuous video. The components of this network can be implemented using any algorithm that possesses three key capabilities: prediction of a signal over time, reduction of signal dimensionality (compression) and the ability to use supplementary contextual information to inform the prediction. The presented architecture is highly parallelized and scalable, and is implemented using localized connectivity, processing, and learning. We demonstrate an implementation of this architecture where the components are built from multi layer perceptrons. We apply the implementation to create a system capable of stable and robust visual tracking of objects as seen by a moving camera. Results show performance on par with or exceeding state of the art tracking algorithms. The tracker can be trained in either fully supervised or unsupervised then briefly supervised regimes. Success of the briefly supervised regime suggests that the unsupervised portion of the model extracts useful information about visual reality. 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.
ArXiv