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Perception Updating Networks: On architectural constraints for interpretable video generative models
Decoupled "what" and "where" variational statistical framework and equivalent multi-stream network
Feb 12, 2017 (modified: Feb 12, 2017)ICLR 2017 workshop submissionreaders: everyone
Abstract:We investigate a neural network architecture and statistical framework that models frames in videos using principles inspired by computer graphics pipelines. The proposed model explicitly represents "sprites" or its percepts inferred from maximum likelihood of the scene and infers its movement independently of its content. We impose architectural constraints that forces resulting architecture to behave as a recurrent what-where prediction network.