Methods › Computer Vision › Reversible Image Conversion Models › IICNet
IICNet
Introduced by Ka Leong Cheng et al. in IICNet: A Generic Framework for Reversible Image Conversion
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Invertible Image Conversion Net, or IICNet, is a generic framework for reversible image conversion tasks. Unlike previous encoder-decoder based methods, IICNet maintains a highly invertible structure based on invertible neural networks (INNs) to better preserve the information during conversion. It uses a relation module and a channel squeeze layer to improve the INN nonlinearity to extract cross-image relations and the network flexibility, respectively.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars 17 May 2023 · 0 repositories · arXiv:2305.10217
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IICNet: A Generic Framework for Reversible Image Conversion 9 Sep 2021 · 1 repository · arXiv:2109.04242
Tasks archive 2025-07-28
4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Classification | 1 |
| Decoder | 1 |
| Image Classification | 1 |
| image-classification | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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