PreCNet: Next-Frame Video Prediction Based on Predictive Coding
Abstract
Predictive coding, currently a highly influential theory in neuroscience, has not been widely adopted in machine learning yet. In this work, we transform the seminal model of Rao and Ballard (1999) into a modern deep learning framework while remaining maximally faithful to the original schema. The resulting network we propose (PreCNet) is tested on a widely used next frame video prediction benchmark, which consists of images from an urban environment recorded from a car-mounted camera, and achieves state-of-the-art performance. Performance on all measures (MSE, PSNR, SSIM) was further improved when a larger training set (2M images from BDD100k), pointing to the limitations of the KITTI training set. This work demonstrates that an architecture carefully based in a neuroscience model, without being explicitly tailored to the task at hand, can exhibit exceptional performance.
Cite
@article{arxiv.2004.14878,
title = {PreCNet: Next-Frame Video Prediction Based on Predictive Coding},
author = {Zdenek Straka and Tomas Svoboda and Matej Hoffmann},
journal= {arXiv preprint arXiv:2004.14878},
year = {2023}
}
Comments
Accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS)