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ReNAS: Relativistic Evaluation of Neural Architecture Search

CVPR 2021 · Architectures and efficiency › Neural architecture search · approach: Efficient NAS search strategies · 84 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use pairwise ranking loss instead of MSE (ablation)ReNAS predictor with type-matrix features · NAS-Bench-101, 0.1% predictor training samplesKendall's Tau: 0.3465 -> 0.6039Helped
Use pairwise ranking loss instead of MSE (ablation)ReNAS predictor with feature-tensor inputs · NAS-Bench-101, 0.1% predictor training samplesKendall's Tau: 0.4856 -> 0.6335Helped
Add FLOPs and parameter information in the feature tensor (ablation)ReNAS predictor with MSE loss · NAS-Bench-101, 0.1% predictor training samplesKendall's Tau: 0.3465 -> 0.4856Helped
Add continuity loss to pairwise ranking loss (ablation)ReNAS predictor with type-matrix inputs · NAS-Bench-101, 0.1% predictor training samplesKendall's Tau: 0.6039 -> 0.6096Helped
Add continuity loss to pairwise ranking loss (ablation)ReNAS predictor with feature-tensor inputs · NAS-Bench-101, 0.1% predictor training samplesKendall's Tau: 0.6335 -> 0.6574Helped
Use random rather than parameter-count-based sample selectionReNAS predictor training · NAS-Bench-101, CIFAR-10 testaccuracy (%): 93.84 ± 0.21 -> 93.95 ± 0.11Helped
Use random rather than FLOPs-based sample selectionReNAS predictor training · NAS-Bench-101, CIFAR-10 testaccuracy (%): 93.76 ± 0.13 -> 93.95 ± 0.11Helped
Use ReNAS predictor-guided search instead of REANAS-Bench-201 search · CIFAR-10 testaccuracy (%): 93.92±0.30 -> 93.99±0.25Helped

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