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DLOW: Domain Flow for Adaptation and Generalization

CVPR 2019 · Label-efficient and robust learning › Domain adaptation and generalization · approach: Domain-generalized segmentation · 375 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use DLOW intermediate-domain images instead of target-style-only imagesPixel-level segmentation training on GTA5 · GTA5 → CityscapesmIoU: 40.7 to 42.3Helped
Set z=1 for all translated source images rather than sampling intermediate domainness (ablation)DLOW pixel-level segmentation training · GTA5 → CityscapesmIoU: 42.3 -> 40.7Hurt
Replace original source images with DLOW-translated intermediate-domain imagesAdaptSegNet · GTA5 → CityscapesmIoU: 42.4 -> 44.8Helped
Replace original source images with DLOW-translated intermediate-domain imagesAdaptSegNet · GTA5 → KITTImIoU: 30.7 -> 36.6Helped
Replace original source images with DLOW-translated intermediate-domain imagesAdaptSegNet · GTA5 → WildDashmIoU: 18.9 -> 24.9Helped
Replace original source images with DLOW-translated intermediate-domain imagesAdaptSegNet · GTA5 → BDD100KmIoU: 37.0 -> 39.1Helped
Use DLOW intermediate-domain images instead of target-style-only imagesPixel-level segmentation training on SYNTHIA · SYNTHIA → CityscapesmIoU: 41.6 to 42.8Helped
Set z=1 for all translated source images rather than sampling intermediate domainness (ablation)DLOW pixel-level segmentation training · SYNTHIA → CityscapesmIoU: 42.8 -> 41.6Hurt
Replace original source images with DLOW-translated intermediate-domain imagesAdaptSegNet · SYNTHIA → CityscapesmIoU: 45.7% to 47.1%Helped
Replace original source images with DLOW-translated intermediate-domain imagesAdaptSegNet · SYNTHIA → WildDashmIoU: 20.0 -> 24.4Helped
Replace original source images with DLOW-translated intermediate-domain imagesAdaptSegNet · SYNTHIA → BDD100KmIoU: 30.8 -> 35.3Helped
Use DLOW instead of FadNet for photo-to-art style transferPhoto-to-art image translation · Photo → Van Goghuser preference: 1.4% / 98.6%Helped

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