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Research map / Learning theory and algorithms / Sample complexity

Fast Fourier Color Constancy

CVPR 2017 · Learning theory and algorithms › Sample complexity · approach: Density and property estimation · 214 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Replace CCC with FFCC Model JColor constancy on Gehler-Shi · Gehler-Shiaverage angular error: 1.40 -> 1.20Helped
Add metadata and semantic features (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 1.07Helped
Add metadata (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 1.10Helped
Add semantic features (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 1.15Helped
Remove frequency-domain preconditioning (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 2.18Hurt
Remove total variation regularization (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 1.30Hurt
Remove BVM loss (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 1.78Hurt
Remove illuminant gain and bias maps (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 1.53Hurt
Remove regularization (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 1.70Hurt
Use gray-world rather than gray-light de-aliasing (ablation)FFCC Model J · Gehler-Shiaverage angular error: 1.20 -> 1.24Hurt
Use 4 channels instead of 2 (ablation)FFCC · Gehler-Shiaverage angular error: 1.20 -> 1.21No clear effect
Use thumbnail input rather than full input (ablation)FFCC · Gehler-Shiaverage angular error: 1.20 -> 1.37Hurt

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