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Arch-Graph: Acyclic Architecture Relation Predictor for Task-Transferable Neural Architecture Search

CVPR 2022 · Graph learning › Graph algorithms and clustering · approach: Neural combinatorial optimization on graphs · 23 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the MWAS graph ordering instead of Arch-Graph-zero insertion-sort ordering (ablation)Arch-Graph · TransNAS-Bench-101 macroaverage rank: 7.83 -> 5.24; improves the rank by 3.14 on averageHelped
Use the MWAS graph ordering instead of Arch-Graph-zero insertion-sort ordering (ablation)Arch-Graph · TransNAS-Bench-101 microaverage rank: 14.7 -> 12.2; improves the rank by 5.28 on averageHelped
Use Task2Vec task embeddings rather than randomly initialized task vectors (ablation)Arch-Graph · TransNAS-Bench-101, six target tasksaverage rank: 24.13 -> 5.24Helped
Use Task2Vec task embeddings rather than randomly initialized task vectors (ablation)Arch-Graph · TransNAS-Bench-101, six target tasksvariance of average rank: 692.03 -> 0.63Helped
Transfer the predictor from a source task instead of pretraining and finetuning on the same task (ablation)Arch-Graph · TransNAS-Bench-101 macroaverage rank: 12.68 -> 5.24Helped
Transfer the predictor from a source task instead of pretraining and finetuning on the same task (ablation)Arch-Graph · TransNAS-Bench-101 microaverage rank: 22.15 -> 12.2Helped
Use Arch-Graph rather than weakNASNAS search on CIFAR-100 · NAS-Bench-201 CIFAR-100accuracy: 73.42 -> 73.38Hurt
Use Arch-Graph rather than weakNASNAS search on CIFAR-100 · NAS-Bench-201 CIFAR-100Kendall rank coefficient: 0.49 -> 0.67Helped
Use Arch-Graph rather than weakNAS-tTransfer NAS · TransNAS-Bench-101 macroaverage rank: 15.43 -> 5.24Helped
Use Arch-Graph rather than weakNAS-tTransfer NAS · TransNAS-Bench-101 microaverage rank: 35.73 -> 12.2Helped

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