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

TL;DR

This paper introduces TE-NAS, a training-free neural architecture search method that uses NTK spectrum and linear regions to efficiently identify high-performing architectures without training, significantly reducing search costs.

Contribution

The paper proposes a novel training-free NAS framework based on NTK spectrum and linear regions, enabling architecture ranking without training and reducing resource consumption.

Findings

TE-NAS achieves high-quality architecture search in 0.5 to 4 GPU hours.

The proposed measurements correlate strongly with network test accuracy.

TE-NAS outperforms traditional NAS methods in efficiency and effectiveness.

Abstract

Neural Architecture Search (NAS) has been explosively studied to automate the discovery of top-performer neural networks. Current works require heavy training of supernet or intensive architecture evaluations, thus suffering from heavy resource consumption and often incurring search bias due to truncated training or approximations. Can we select the best neural architectures without involving any training and eliminate a drastic portion of the search cost? We provide an affirmative answer, by proposing a novel framework called training-free neural architecture search (TE-NAS). TE-NAS ranks architectures by analyzing the spectrum of the neural tangent kernel (NTK) and the number of linear regions in the input space. Both are motivated by recent theory advances in deep networks and can be computed without any training and any label. We show that: (1) these two measurements imply the trainability and expressivity of a neural network; (2) they strongly correlate with the network's test accuracy. Further on, we design a pruning-based NAS mechanism to achieve a more flexible and superior trade-off between the trainability and expressivity during the search. In NAS-Bench-201 and DARTS search spaces, TE-NAS completes high-quality search but only costs 0.5 and 4 GPU hours with one 1080Ti on CIFAR-10 and ImageNet, respectively. We hope our work inspires more attempts in bridging the theoretical findings of deep networks and practical impacts in real NAS applications. Code is available at: https://github.com/VITA-Group/TENAS.