Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342650" target="_blank" >RIV/68407700:21230/20:00342650 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR42600.2020.00792" target="_blank" >https://doi.org/10.1109/CVPR42600.2020.00792</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR42600.2020.00792" target="_blank" >10.1109/CVPR42600.2020.00792</a>
Alternative languages
Result language
angličtina
Original language name
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
Original language description
It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main difficulties in implementing this in practice are associated with a discrepancy in suitable learning objectives as well as with the necessity of approximations for the inference. In this work we take one of the simplest inference methods, a truncated max-product Belief Propagation, and add what is necessary to make it a proper component of a deep learning model: connect it to learning formulations with losses on marginals and compute the backprop operation. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs), allowing us to design a hierarchical model composing BP inference and CNNs at different scale levels. The model is applicable to a range of dense prediction problems, is well-Trainable and provides parameter-efficient and robust solutions in stereo, flow and semantic segmentation.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
<a href="/en/project/EF18_070%2F0010457" target="_blank" >EF18_070/0010457: International Mobility of Researchers MSCA-IF II in CTU in Prague</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition
ISBN
978-1-7281-7169-2
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
10
Pages from-to
7897-7906
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Seattle
Event date
Jun 13, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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