Perception Updating Networks: On architectural constraints for interpretable video generative models

Eder Santana; Jose C Principe

ICLR 2017workshop0 citations

Perception Updating Networks: On architectural constraints for interpretable video generative models

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.

Structured predictionUnsupervised Learning

BibTeX

@misc{
lee2017making,
title={Making Stochastic Neural Networks from Deterministic Ones},
author={Kimin Lee and Jaehyung Kim and Song Chong and Jinwoo Shin},
year={2017},
url={https://openreview.net/forum?id=B1akgy9xx}
}