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Contrastive Semi-Supervised Learning for Underwater Image Restoration via Reliable Bank

CVPR 2023 · Label-efficient and robust learning › Semi-supervised learning · approach: Pseudo-labeling for specialized tasks · 269 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use the full Semi-UIR framework instead of supervised-only AIM-Net (ablation)AIM-Net · testRPSNR: 24.38 -> 24.59Helped
add mean-teacher consistency training without reliable bank or contrastive loss (ablation)AIM-Net · testRPSNR: 24.38 -> 23.11Hurt
add the reliable bank to mean-teacher training (ablation)Semi-base · testRPSNR: 23.11 -> 24.53Helped
add the reliable bank to mean-teacher training (ablation)Semi-base · EUVPMUSIQ: 42.48 -> 43.27Helped
add contrastive loss without a reliable bank (ablation)Semi-base · EUVPMUSIQ: 42.48 -> 46.59Helped
add contrastive loss without a reliable bank (ablation)Semi-base · UIEBMUSIQ: 40.33 -> 40.64No clear effect
use MUSIQ rather than NIMA to select reliable-bank outputs (ablation)Semi-UIR · testRPSNR: 23.88 -> 24.59Helped
use MUSIQ rather than PAQ2PIQ to select reliable-bank outputs (ablation)Semi-UIR · testRPSNR: 24.28 -> 24.59Helped
apply color jitter augmentation (ablation)Semi-UIR · EUVPMUSIQ: 46.06 -> 49.16Helped
apply Gaussian blur augmentation (ablation)Semi-UIR · RUIEMUSIQ: 31.14 -> 36.88Helped
apply grayscale augmentation (ablation)Semi-UIR · SeathruMUSIQ: 64.71 -> 65.19Helped
combine color jitter, Gaussian blur, and grayscale augmentations (ablation)Semi-UIR · EUVPMUSIQ: 46.06 -> 51.66Helped

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