Abstract
A key component in Neural Architecture Search (NAS) is an accuracy predictor which asserts the accuracy of a queried architecture. To build a high quality accuracy predictor, conventional NAS algorithms rely on training a mass of architectures or a big supernet. This step often consumes hundreds to thousands of GPU days, dominating the total search cost. To address this issue, we propose to replace the accuracy predictor with a novel model-complexity index named Zen-score. Instead of predicting model accuracy, Zen-score directly asserts the model complexity of a network without training its parameters. This is inspired by recent advances in deep learning theories which show that model complexity of a network positively correlates to its accuracy on the target dataset. The computation of Zen-score only takes a few forward inferences through a randomly initialized network using random Gaussian input. It is applicable to any Vanilla Convolutional Neural Networks (VCN-networks) or compatible variants, covering a majority of networks popular in real-world applications. When combining Zen-score with Evolutionary Algorithm, we obtain a novel Zero-Shot NAS algorithm named Zen-NAS. We conduct extensive experiments on CIFAR10/CIFAR100 and ImageNet. In summary, Zen-NAS is able to design high performance architectures in less than half GPU day (12 GPU hours). The resultant networks, named ZenNets, achieve up to $83.0\%$ top-1 accuracy on ImageNet. Comparing to EfficientNets-B3/B5 of the same or better accuracies, ZenNets are up to $5.6$ times faster on NVIDIA V100, $11$ times faster on NVIDIA T4, $2.6$ times faster on Google Pixel2 and uses $50\%$ less FLOPs. Our source code and pre-trained models are released on this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2102.01063
