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Invertible Rescaling Network

IRN

6 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

An Invertible Rescaling Network (IRN) is a network for image rescaling. According to the Nyquist-Shannon sampling theorem, high-frequency contents are lost during downscaling. Ideally, we hope to keep all lost information to perfectly recover the original HR image, but storing or transferring the high-frequency information is unacceptable. In order to well address this challenge, the Invertible Rescaling Net (IRN) captures some knowledge on the lost information in the form of its distribution and embeds it into model’s parameters to mitigate the ill-posedness. Given an HR image x, IRN not only downscales it into a LR image y, but also embeds the case-specific high-frequency content into an auxiliary case-agnostic latent variable z, whose marginal distribution obeys a fixed pre-specified distribution (e.g., isotropic Gaussian). Based on this model, we use a randomly drawn sample of z from the pre-specified distribution for the inverse upscaling procedure, which holds the most information that one could have in upscaling.

Source: Invertible Image RescalingSee Code · pkuxmq/Invertible-Image-Rescaling

Papers archive 2025-07-28

6 shown of 6, 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.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Image Rescaling4
Image Super-Resolution3
Super-Resolution3
Diagnostic1
Image Restoration1
Medical Diagnosis1
Recommendation Systems1

Usage over time archive 2025-07-28

Papers per year tagged with IRN: 2020 to 2024, peak 2 2 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 2 papers 2022 2023: 2 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (6 dated). Bars are counts, not a trend claim.

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

Image Models

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