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Research map / Architectures and efficiency / Neural architecture search

Customizable Architecture Search for Semantic Segmentation

CVPR 2019 · Architectures and efficiency › Neural architecture search · approach: Latency-aware architecture search · 166 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add ImageNet pre-training (ablation)CAS-GT · Cityscapes valmIoU (%): 68.1 -> 70.4Helped
add the searched MSCell (ablation)ImageNet pre-trained CAS-GT · Cityscapes valmIoU (%): 70.4 -> 74.0Helped
add the searched MSCell (ablation)ImageNet pre-trained CAS-GT · Cityscapes valTime (ms): 23.8 -> 29.2Hurt
replace the searched MSCell with PSP (ablation)ImageNet pre-trained CAS-GT · Cityscapes valmIoU (%): 70.4 -> 71.5Helped
replace the searched MSCell with ASPP (ablation)ImageNet pre-trained CAS-GT · Cityscapes valmIoU (%): 70.4 -> 72.9Helped
replace the searched MSCell with ASPP+ (ablation)ImageNet pre-trained CAS-GT · Cityscapes valmIoU (%): 70.4 -> 73.9Helped
use Cityscapes coarse annotations (ablation)CAS-GT+MSCell · Cityscapes testmIoU (%): 70.5 -> 72.3Helped
optimize architecture using GPU time as the constraint instead of another cost constraintCAS · Cityscapes validationmIoUHelped
increase the tradeoff parameter λCAS · Cityscapes validationnetwork costHelped
increase the tradeoff parameter λCAS · Cityscapes validationmIoUHurt

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