Year: 2024
Type: article
Abstract: Designing a high-performance neural network is a difficult task. Neural architecture search (NAS) methods aim to solve this process. However, the construction of a high-quality accuracy predictor, which is a key component of NAS, usually requires significant computation. Therefore, zero-shot proxy-based NAS methods have been actively and extensively investigated. In this work, we propose a new efficient zero-shot proxy, Incorporated-Score, to rank deep neural network architectures instead of using an accuracy predictor. The proposed Incorporated-Score proxy is generated by incorporating the zen-score and entropy information of the network, and it does not need to train any network. We then introduce an optimal NAS algorithm called Incorporated-NAS that targets the maximization of the Incorporated-Score of the neural network within the specified inference budgets. The experiments show that the network designed by Incorporated-NAS with Incorporated-Score outperforms the previously proposed Zen-NAS and achieves a new SOTAaccuracy on the CIFAR-10, CIFAR-100, and ImageNet datasets with a lightweight scale.
Source: Electronics
Authors: Thi-Trang Nguyen, Ji-Hyeong Han
Institution: Seoul National University of Science and Technology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,728
Awards: G226226466, G4700831490
OpenAlex ID: W4401759872