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A Gradient-Guided Evolutionary Neural Architecture Search.

Yu Xue, Xiaolong Han, Ferrante Neri

IEEE Transactions on Neural Networks and Learning Systems

|March 11, 2024

Summary

This summary is machine-generated.

Gradient-guided evolutionary Neural Architecture Search (NAS) efficiently designs Convolutional Neural Networks (CNNs) for image classification. This method significantly reduces computational cost and time, achieving high performance on benchmark datasets.

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Neural Architecture Search (NAS) automates deep neural network design but is computationally intensive.
Existing NAS methods face limitations in efficiency and performance optimization.

Purpose of the Study:

To propose a novel hybrid algorithm, Gradient-guided Evolutionary NAS (GENAS), for efficient Convolutional Neural Network (CNN) design.
To address the computational expense and limitations of traditional NAS and gradient descent methods.

Main Methods:

GENAS combines evolutionary global and local search operators on a population of subnets sampled from a supernet.
Candidate architectures are encoded in a table, manipulated using novel crossover and mutation operators.
A local search inspired by differentiable NAS is applied to candidate architectures without retraining.

Main Results:

GENAS achieved low test errors: 2.45% (CIFAR-10), 16.86% (CIFAR-100), and 23.9% (ImageNet).
The method demonstrated significant computational efficiency, requiring only 0.26 GPU days.
Decoupled subnet evaluation prevented strong coupling of operations within the supernet.

Conclusions:

GENAS effectively expedites the training and evaluation processes in NAS.
The proposed method successfully obtains high-performance CNN structures for image classification.
GENAS overcomes limitations of purely evolutionary or gradient descent NAS approaches.