Papers › Adaptive Patch Exiting for Scalable Single Image Super-Resolution

Adaptive Patch Exiting for Scalable Single Image Super-Resolution

22 Mar 2022arXiv:2203.11589archive 2025-07-28

Shizun Wang, Jiaming Liu, Kaixin Chen, Xiaoqi Li, Ming Lu, Yandong Guo

Since the future of computing is heterogeneous, scalability is a crucial problem for single image super-resolution. Recent works try to train one network, which can be deployed on platforms with different capacities. However, they rely on the pixel-wise sparse convolution, which is not hardware-friendly and achieves limited practical speedup. As image can be divided into patches, which have various restoration difficulties, we present a scalable method based on Adaptive Patch Exiting (APE) to achieve more practical speedup. Specifically, we propose to train a regressor to predict the incremental capacity of each layer for the patch. Once the incremental capacity is below the threshold, the patch can exit at the specific layer. Our method can easily adjust the trade-off between performance and efficiency by changing the threshold of incremental capacity. Furthermore, we propose a novel strategy to enable the network training of our method. We conduct extensive experiments across various backbones, datasets and scaling factors to demonstrate the advantages of our method. Code is available at https://github.com/littlepure2333/APE

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littlepure2333/ape officialmentioned in paperpytorch report

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13 samples harvested; 6 ran; 0 honoured the contract we drafted; 7 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
2ran · fixture could not drive it
3ran
7unverified

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DropPath littlepure2333/APE/model/swinir_ape.py official repository ran · metamorphic tier: deterministic fingerprinted no licence file found · pointer only · 2c28ad5498af1d70 · report
Upsample littlepure2333/APE/model/swinir_ape.py official repository ran · metamorphic tier: deterministic no licence file found · pointer only · ed05c8ce2b7bd1c7 · report
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BasicLayer littlepure2333/APE/model/swinir_ape.py official repository unverified no licence file found · pointer only · 78b5f0f450e9e964 · report
PatchEmbed littlepure2333/APE/model/swinir_ape.py official repository unverified no licence file found · pointer only · 58479267b4a85427 · report
PatchUnEmbed littlepure2333/APE/model/swinir_ape.py official repository unverified no licence file found · pointer only · bafcd1f59633494d · report
RSTB littlepure2333/APE/model/swinir_ape.py official repository unverified no licence file found · pointer only · c360863fc4569772 · report
SwinIR littlepure2333/APE/model/swinir_ape.py official repository unverified no licence file found · pointer only · f390c6e387d560fd · report
SwinTransformerBlock littlepure2333/APE/model/swinir_ape.py official repository unverified no licence file found · pointer only · 55a5055bb235b697 · report
WindowAttention littlepure2333/APE/model/swinir_ape.py official repository unverified no licence file found · pointer only · fabc3c0b8a7bedc7 · report
drop_path identical code first harvested elsewhere ran · fixture could not drive it licence of this copy not recorded · 52d96aa31ed74a56 · report
window_partition identical code first harvested elsewhere ran · fixture could not drive it fingerprinted licence of this copy not recorded · 60363d0620f5778d · report
window_reverse identical code first harvested elsewhere ran · our draft was wrong licence of this copy not recorded · 4f003e9ad9b7a7cb · report

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Image Super-ResolutionSuper-Resolution

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