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One-Shot Neural architecture search (NAS) has received wide attentions due to its computational efficiency. Most state-of-the-art One-Shot NAS methods use the validation accuracy based on inheriting weights from the supernet as the stepping stone to search for the best performing architecture, adopting a bilevel optimization pattern with assuming this validation accuracy approximates to the test accuracy after re-training. However, recent works have found that there is no positive correlationdoi:10.24963/ijcai.2020/437 dblp:conf/ijcai/00010WL20 fatcat:pmbywbnttrbjldqm5g3layhome