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Neural Architecture Search (NAS) has been explosively studied to automate thediscovery of top-performer neural networks. Current works require heavytraining of supernet or intensive architecture evaluations, thus suffering fromheavy resource consumption and often incurring search bias due to truncatedtraining or approximations. Can we select the best neural architectures withoutinvolving any training and eliminate a drastic portion of the search cost? Weprovide an affirmative answer, by proposing a novel framework calledtraining-free neural architecture search (TE-NAS). TE-NAS ranks architecturesby analyzing the spectrum of the neural tangent kernel (NTK) and the number oflinear regions in the input space. Both are motivated by recent theory advancesin deep networks and can be computed without any training and any label. Weshow that: (1) these two measurements imply the trainability and expressivityof a neural network; (2) they strongly correlate with the network's testaccuracy. Further on, we design a pruning-based NAS mechanism to achieve a moreflexible and superior trade-off between the trainability and expressivityduring the search. In NAS-Bench-201 and DARTS search spaces, TE-NAS completeshigh-quality search but only costs 0.5 and 4 GPU hours with one 1080Ti onCIFAR-10 and ImageNet, respectively. We hope our work inspires more attempts inbridging the theoretical findings of deep networks and practical impacts inreal NAS applications. Code is available at:https://github.com/VITA-Group/TENAS.

Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective