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Single-Frame Regularization for Temporally Stable CNNs

CVPR 2019 · Video › Video segmentation and tracking · approach: Spike and neuromorphic deblurring · 31 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add transform-invariance regularization at α=0.95Colorization CNN fine-tuning · YouTube-8M colorizationSmoothness: 0.7243 -> 2.8934Helped
Add transform-invariance regularization at α=0.95Colorization CNN fine-tuning · YouTube-8M colorizationPSNR: 18.5805 -> 18.8880Helped
Add sparse-Jacobian regularization at α=0.95Colorization CNN fine-tuning · YouTube-8M colorizationSmoothness: 0.7243 -> 2.5079Helped
Use α=0.8 rather than α=0.95 for transform invariance (ablation)Colorization CNN fine-tuning · YouTube-8M colorizationSmoothness: 2.8934 -> 1.9074Hurt
Use α=0.8 rather than α=0.95 for transform invariance (ablation)Colorization CNN fine-tuning · YouTube-8M colorizationPSNR: 18.8880 -> 18.9437Helped
Add transform-invariance regularization at α=0.8HDR reconstruction CNN fine-tuning · HDR video reconstructionSmoothness: 5.9951 -> 8.0798Helped
Add transform-invariance regularization at α=0.8HDR reconstruction CNN fine-tuning · HDR video reconstructionPSNR: 25.5131 -> 25.4374Hurt
Add sparse-Jacobian regularization at α=0.95HDR reconstruction CNN fine-tuning · HDR video reconstructionSmoothness: 5.9951 -> 7.3048Helped
Use α=0.8 rather than α=0.95 for transform invariance (ablation)HDR reconstruction CNN fine-tuning · HDR video reconstructionPSNR: 24.1678 -> 25.4374Helped
Use α=0.8 rather than α=0.95 for transform invariance (ablation)HDR reconstruction CNN fine-tuning · HDR video reconstructionSmoothness: 10.6435 -> 8.0798Hurt
Use noise perturbations instead of geometric transformations for stability regularization (ablation)CNN temporal regularization · Colorization and HDR reconstructionSmoothnessHurt
Use naive geometric augmentation instead of explicit temporal regularization (ablation)CNN training · Colorization and HDR reconstructionSmoothnessHurt

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