PreCNet: Next-Frame Video Prediction Based on Predictive Coding
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00365341" target="_blank" >RIV/68407700:21230/24:00365341 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/TNNLS.2023.3240857" target="_blank" >https://doi.org/10.1109/TNNLS.2023.3240857</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TNNLS.2023.3240857" target="_blank" >10.1109/TNNLS.2023.3240857</a>
Alternative languages
Result language
angličtina
Original language name
PreCNet: Next-Frame Video Prediction Based on Predictive Coding
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE Transactions on Neural Networks and Learning Systems
ISSN
2162-237X
e-ISSN
2162-2388
Volume of the periodical
35
Issue of the periodical within the volume
8
Country of publishing house
US - UNITED STATES
Number of pages
15
Pages from-to
10353-10367
UT code for WoS article
000932859200001
EID of the result in the Scopus database
2-s2.0-85148422306