ClassSR is a framework to accelerate super-resolution (SR) networks on large images (2K-8K). ClassSR combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions.
Source: ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data CharacteristicPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Classification | 1 | 33.33% |
General Classification | 1 | 33.33% |
Super-Resolution | 1 | 33.33% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |