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Recent years have witnessed a surging interest in Neural Architecture Search(NAS). Various algorithms have been proposed to improve the search efficiencyand effectiveness of NAS, i.e., to reduce the search cost and improve thegeneralization performance of the selected architectures, respectively.However, the search efficiency of these algorithms is severely limited by theneed for model training during the search process. To overcome this limitation,we propose a novel NAS algorithm called NAS at Initialization (NASI) thatexploits the capability of a Neural Tangent Kernel in being able tocharacterize the converged performance of candidate architectures atinitialization, hence allowing model training to be completely avoided to boostthe search efficiency. Besides the improved search efficiency, NASI alsoachieves competitive search effectiveness on various datasets like CIFAR-10/100and ImageNet. Further, NASI is shown to be label- and data-agnostic under mildconditions, which guarantees the transferability of architectures selected byour NASI over different datasets.

NASI: Label- and Data-agnostic Neural Architecture Search at Initialization