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Searching for Efficient Multi-Scale Architectures for Dense Image Prediction

NeurIPS 2018 · Detection and segmentation › CNN features · approach: Efficient network training and design · 348 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the searched DPC instead of DeepLabv3+Modified Xception dense prediction model · Cityscapes testmIOU: 82.1 -> 82.7Helped
Use the searched DPC instead of the strongest listed prior modelModified Xception dense prediction model · PASCAL-Person-Part validationmIOU: 67.60 -> 71.34Helped
Use the searched DPC instead of MSCIModified Xception dense prediction model · PASCAL VOC 2012 testmIOU: 88.0 -> 87.9Hurt
Replace ASPP with DPCMobileNet-v2 model · Cityscapes validationmIOU: 73.97 -> 75.38Helped
Replace ASPP with DPCModified Xception model · Cityscapes validationmIOU: 80.25 -> 80.85Helped
Replace ASPP with DPCModified Xception model · Cityscapes validationParams: 1.59M -> 0.81MHelped
Replace ASPP with DPCModified Xception model · Cityscapes validationMAdds: 18.12B -> 6.84BHelped
Use MobileNet-v2 instead of the larger Xception backbone for the proxy (ablation)Architecture ranking on Cityscapes · Cityscapes proxy taskSpearman rank correlation: ρ = 0.36Mixed
Cache backbone activations instead of training end-to-end (ablation)MobileNet-v2 architecture-search proxy · Cityscapes proxy taskSpearman rank correlation: ρ = 0.47Helped
Train proxy candidates for 30K iterations rather than to convergence (ablation)DPC architecture-search proxy · Dense prediction architecture rankingruntime: 1+ week -> 90 minutes; 100+-foldHelped

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