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DANICE: Domain adaptation without forgetting in neural image compression
[article]
2021
arXiv
pre-print
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In this paper, we study the adaptability of codecs to custom domains of interest. We show that NIC codecs are transferable and that they can be adapted with relatively few target domain images. ...
However, naive adaptation interferes with the solution optimized for the original source domain, resulting in forgetting the original coding capabilities in that domain, and may even break the compatibility ...
Adapting to new domains We introduce the problem of domain adaptation in neural image compression (DANICE), where a codec trained on a source domain X 1 is leveraged to improve compression in a target ...
arXiv:2104.09370v1
fatcat:nte4zosbpvedzcvgvhihlh6czm
Table of Contents
2021
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
A Two-Stage Deep Network for High Dynamic Range Image Reconstruction 550 ...
Adaptation without Forgetting in Neural Image Compression
1921
Sudeep Katakol (Univ. of Michigan, Ann Arbor), Luis Herranz (Computer
Vision Center, UAB, Barcelona), Fei Yang (Computer Vision Center ...
and
Technology of China), and Zhibo Chen (CAS Key Laboratory of Technology
in Geo-spatial Information Processing and Application System,
University of Science and Technology of China)
DANICE: Domain ...
doi:10.1109/cvprw53098.2021.00004
fatcat:yh3zw4afzneeza6jrd6377fd3y