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In this paper, we investigate a new variant of neural architecture search(NAS) paradigm -- searching with random labels (RLNAS). The task soundscounter-intuitive for most existing NAS algorithms since random label providesfew information on the performance of each candidate architecture. Instead, wepropose a novel NAS framework based on ease-of-convergence hypothesis, whichrequires only random labels during searching. The algorithm involves two steps:first, we train a SuperNet using random labels; second, from the SuperNet weextract the sub-network whose weights change most significantly during thetraining. Extensive experiments are evaluated on multiple datasets (e.g.NAS-Bench-201 and ImageNet) and multiple search spaces (e.g. DARTS-like andMobileNet-like). Very surprisingly, RLNAS achieves comparable or even betterresults compared with state-of-the-art NAS methods such as PC-DARTS, SinglePath One-Shot, even though the counterparts utilize full ground truth labelsfor searching. We hope our finding could inspire new understandings on theessential of NAS.

Neural Architecture Search with Random Labels