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Learning a Sparse Transformer Network for Effective Image Deraining

CVPR 2023 · Architectures and efficiency › Attention and transformers · approach: Efficient image transformers · 525 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace dense self-attention with learnable top-k sparse attention (ablation)DRSformer · deraining benchmarksPSNRHelped
use a learnable interval for k rather than a fixed k value (ablation)DRSformer TKSA · Rain200HPSNR: 32.18 dBHelped
make k too small (ablation)DRSformer TKSA · Rain200HPSNRHurt
increase k enough to include irrelevant attention features (ablation)DRSformer TKSA · Rain200HPSNRHurt
replace conventional feed-forward network with MSFN (ablation)DRSformer · Rain200HPSNR: 31.84 -> 32.18Helped
replace Dconv feed-forward network with MSFN (ablation)DRSformer · Rain200HPSNR: 31.88 -> 32.18Helped
replace gated-Dconv feed-forward network with MSFN (ablation)DRSformer · Rain200HPSNR: +0.21 dBHelped
add MEFC-1 only with 8 experts (ablation)DRSformer · Rain200HPSNR: 32.03 -> 32.01Hurt
add MEFC-2 only with 8 experts (ablation)DRSformer · Rain200HPSNR: 32.03 -> 32.07Helped
use both MEFC modules with 8 experts instead of one expert (ablation)DRSformer · Rain200HPSNR: 32.06 -> 32.18Helped
increase the experts in both MEFC modules from 4 to 8 (ablation)DRSformer · Rain200HPSNR: 32.14 -> 32.18Helped
use DRSformer instead of RestormerDRSformer · Rain200LPSNR: 40.99 -> 41.23Helped

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