Journal article
COIN++: neural compression across modalities
- Abstract:
-
Neural compression algorithms are typically based on autoencoders that require specialized encoder and decoder architectures for different data modalities. In this paper, we propose COIN++, a neural compression framework that seamlessly handles a wide range of data modalities. Our approach is based on converting data to implicit neural representations, i.e. neural functions that map coordinates (such as pixel locations) to features (such as RGB values). Then, instead of storing the weights of...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Version of record, pdf, 20.9MB)
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- Publication website:
- https://openreview.net/forum?id=NXB0rEM2Tq
Authors
Funding
+ Engineering and Physical Sciences Research Council
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Grant:
56726
EP/R013616/1
Bibliographic Details
- Publisher:
- Journal of Machine Learning Research Publisher's website
- Journal:
- Transactions on Machine Learning Research Journal website
- Volume:
- 2022
- Issue:
- 11
- Article number:
- 485
- Publication date:
- 2022-12-07
- Acceptance date:
- 2022-12-07
Item Description
- Language:
- English
- Keywords:
- Pubs id:
-
1312361
- Local pid:
- pubs:1312361
- Deposit date:
- 2022-12-08
Terms of use
- Copyright date:
- 2022
- Rights statement:
- This is an open access article under a Creative Commons license.
- Licence:
- CC Attribution (CC BY)
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