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DOTS: Decoupling Operation and Topology in Differentiable Architecture Search

CVPR 2021 · Architectures and efficiency › Neural architecture search · approach: Differentiable architecture search (DARTS) · 55 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add explicit topology search to DARTS (ablation)DARTS · CIFAR100test accuracy (%): 80.74 -> 83.07Helped
Add topology search to gradient-based NAS (ablation)DARTS · CIFAR10test accuracy (%): 97.02±0.12 -> 97.40±0.09Helped
Use topology search with the existing method (ablation)GDAS · CIFAR100test accuracy (%): 82.75 -> 83.01Helped
Replace PC-DARTS edge weights with edge-combination topology weights (ablation)DOTS · CIFAR100, 2-edge constrainttest accuracy (%): 82.98 -> 83.72Helped
Remove the fixed edge-number constraint (ablation)DOTS · CIFAR100test accuracy (%): 83.72 -> 83.92Helped
Use edge-combination weights instead of edge-level sigmoid weights (ablation)DOTS · CIFAR100, arbitrary edgestest accuracy (%): 81.02 -> 83.92Helped
Preserve topology-related operations alongside topology-agnostic operations (ablation)DOTS operation search · CIFAR100test accuracy (%): 83.07 -> 83.72Helped
Retain two strongest operations per edge without grouping (ablation)DOTS operation search · CIFAR100test accuracy (%): 83.07 -> 82.96No clear effect
Add more groups among topology-agnostic operations (ablation)DOTS operation search · CIFAR100test accuracy (%): 83.72 -> 83.55No clear effect
Search with edge-combination weights rather than operation weights for topology rankingDOTS · CIFAR10 and CIFAR100Kendall Tau: Tau=0.73 and Tau=0.71Helped
Search the architecture directly on ImageNet rather than transferring a CIFAR10-searched cellDOTS · ImageNettop-1 accuracy (%): 75.7 -> 76.0Helped

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