[share] Zen-NAS: A Zero-Shot NAS for High-Performance Image Recognition

Keywords: Neural Architecture Search (NAS)

ZenNets top-1 accuracy v.s. inference latency (milliseconds per image) on ImageNet. Benchmarked on NVIDIA V100 GPU, half precision (FP16), batch size 64, searching cost 0.5 GPU day.

Abstract

Accuracy predictor is a key component in Neural Architecture Search (NAS) for ranking architectures. Building a high-quality accuracy predictor usually costs enormous computation. To address this issue, instead of using an accuracy predictor, we propose a novel zero-shot index dubbed Zen-Score to rank the architectures. The Zen-Score represents the network expressivity and positively correlates with the model accuracy. The calculation of Zen-Score only takes a few forward inferences through a randomly initialized network, without training network parameters. Built upon the Zen-Score, we further propose a new NAS algorithm, termed as Zen-NAS, by maximizing the Zen-Score of the target network under given inference budgets. Within less than half GPU day, Zen-NAS is able to directly search high performance architectures in a data-free style. Comparing with previous NAS methods, the proposed Zen-NAS is magnitude times faster on multiple server-side and mobile-side GPU platforms with state-of-the-art accuracy on ImageNet. Searching and training code as well as pre-trained models are available from https://github.com/ idstcv/ZenNAS.

Reference

1. #arXiv. https://arxiv.org/pdf/2102.01063v4.pdf
2. #github. https://github.com/ idstcv/ZenNAS

Read more

1. Neural Architecture Search on ImageNet. #paperwithcode. https://paperswithcode.com/sota/neural-architecture-search-on-imagenet