SGAS: Sequential Greedy Architecture Search

Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Müller, Ali Kassem Thabet, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Architecture design has become a crucial component of successful deep learning. Recent progress in automatic neural architecture search (NAS) shows a lot of promise. However, discovered architectures often fail to generalize in the final evaluation. Architectures with a higher validation accuracy during the search phase may perform worse in the evaluation. Aiming to alleviate this common issue, we introduce sequential greedy architecture search (SGAS), an efficient method for neural architecture search. By dividing the search procedure into sub-problems, SGAS chooses and prunes candidate operations in a greedy fashion. We apply SGAS to search architectures for Convolutional Neural Networks (CNN) and Graph Convolutional Networks (GCN). Extensive experiments show that SGAS is able to find state-of-the-art architectures for tasks such as image classification, point cloud classification and node classification in protein-protein interaction graphs with minimal computational cost.
Original languageEnglish (US)
Title of host publication2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
ISBN (Print)978-1-7281-7169-2
DOIs
StatePublished - 2020

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