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Research map / Architectures and efficiency / Neural architecture search

Removing Raindrops and Rain Streaks in One Go

CVPR 2021 · Architectures and efficiency › Neural architecture search · approach: AutoML pipeline search · 202 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
search RSR and RDR block architectures instead of selecting operations randomly (ablation)CCN · RainDS-Syn RSPSNR: 30.72 -> 35.12Helped
search RSR and RDR block architectures instead of selecting operations randomly (ablation)CCN · RainDS-Syn RDPSNR: 29.43 -> 33.89Helped
search RSR and RDR block architectures instead of selecting operations randomly (ablation)CCN · RainDS-Syn RDSPSNR: 28.17 -> 32.27Helped
use complementary cascaded RSR/RDR blocks rather than RSR-only blocks (ablation)CCN · RainDS-Syn RDSPSNR: 30.07 -> 32.27Helped
use complementary cascaded RSR/RDR blocks rather than RDR-only blocks (ablation)CCN · RainDS-Syn RDSPSNR: 29.39 -> 32.27Helped
change the NAS initialization across four search runs (ablation)CCN · RainDS-Syn RDSPSNR: 32.47; 32.27; 31.79; 32.53No clear effect
use CCN rather than the strongest reported baselineCCN · RainDS real RDSPSNR: 21.20 -> 23.09Helped
use CCN rather than the strongest reported baselineCCN · Rain200HPSNR: 28.16 -> 29.12Helped

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