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Research map / Learning theory and algorithms / Prediction and decision losses

Learning to Autofocus

CVPR 2020 · Learning theory and algorithms › Prediction and decision losses · approach: Loss landscape analysis · 72 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the learned model instead of the strongest contrast-based baselineFocal-stack green-channel autofocus · Focal stack green-channel (I*)MAE: 2.056 -> 1.600Helped
Use the learned model instead of the strongest stereo-matching baselineFocal-stack dual-pixel autofocus · Focal stack dual-pixel (D*)MAE: 2.731 -> 1.611Helped
Use the learned model instead of the best single-slice learned-depth baselineSingle-slice dual-pixel autofocus · Single-slice dual-pixel (D1)MAE: 7.176 -> 2.235Helped
Add a second autofocus step (ablation)Our Model · Multi-step D1MAE: 2.235 -> 1.931Helped
Add a second autofocus step (ablation)Our Model · Multi-step I1MAE: 4.321 -> 2.855Helped
Use dual-pixel input instead of green-channel input (ablation)Our single-slice model · Single-slice autofocusMAE: 4.321 -> 2.235Helped
Train and evaluate on registered focal stacks (ablation)Our Model · Focal stack green-channel (I*)MAE: 1.600 -> 1.570Helped
Replace the original-camera test set with the other-phone test set (ablation)Single-slice dual-pixel autofocus · Other-phone test setMAE: 2.235 -> 2.912Mixed
Use low-light scenes instead of light scenes (ablation)Our single-slice dual-pixel model · Single-slice dual-pixel (D1)MAE: 2.654 -> 3.479Hurt

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