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Papers/A Generic Graph-based Neural Architecture Encoding Scheme ...

A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS

Xuefei Ning, Yin Zheng, Tianchen Zhao, Yu Wang, Huazhong Yang

2020-04-04ECCV 2020 8Neural Architecture Search
PaperPDFCode(official)

Abstract

This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from existing graph-based schemes, GATES models the operations as the transformation of the propagating information, which mimics the actual data processing of neural architecture. GATES is a more reasonable modeling of the neural architectures, and can encode architectures from both the "operation on node" and "operation on edge" cell search spaces consistently. Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor. Furthermore, equipped with the improved performance predictor, the sample efficiency of the predictor-based neural architecture search (NAS) flow is boosted. Codes are available at https://github.com/walkerning/aw_nas.

Results

TaskDatasetMetricValueModel
Neural Architecture SearchCIFAR-10 Image ClassificationPercentage error2.58GATES + c/o
Neural Architecture SearchImageNetAccuracy75.9GATES
Neural Architecture SearchImageNetTop-1 Error Rate24.1GATES
AutoMLCIFAR-10 Image ClassificationPercentage error2.58GATES + c/o
AutoMLImageNetAccuracy75.9GATES
AutoMLImageNetTop-1 Error Rate24.1GATES

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