@inproceedings{a353a484bb3d45f384ad408a34c7c990,
title = "FOX-NAS: Fast, On-device and Explainable Neural Architecture Search",
abstract = "Neural architecture search can discover neural networks with good performance, and One-Shot approaches are prevalent. One-Shot approaches typically require a supernet with weight sharing and predictors that predict the performance of architecture. However, the previous methods take much time to generate performance predictors thus are inefficient. To this end, we propose FOX-NAS that consists of fast and explainable predictors based on simulated annealing and multivariate regression. Our method is quantization-friendly and can be efficiently deployed to the edge. The experiments on different hardware show that FOX-NAS models outperform some other popular neural network architectures. For example, FOX-NAS matches MobileNetV2 and EfficientNet-Lite0 accuracy with 240% and 40% less latency on the edge CPU. Search code and pre-trained models are released at https://github.com/great8nctu/FOX-NAS.1",
author = "Liu, {Chia Hsiang} and Han, {Yu Shin} and Sung, {Yuan Yao} and Yi Lee and Chiang, {Hung Yueh} and Wu, {Kai Chiang}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 ; Conference date: 11-10-2021 Through 17-10-2021",
year = "2021",
doi = "10.1109/ICCVW54120.2021.00093",
language = "English",
series = "Proceedings of the IEEE International Conference on Computer Vision",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "789--797",
booktitle = "Proceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021",
address = "美國",
}