Perception Updating Networks: On architectural constraints for interpretable video generative modelsDownload PDF

Eder Santana, Jose C Principe (privately revealed to you)

02 Nov 2024 (modified: 21 Jul 2022)ICLR 2017 Invite to WorkshopReaders: 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.
Keywords: Structured prediction, Unsupervised Learning
Conflicts: ufl.edu
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