Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

Konsep Inti

The author proposes a Pareto-wise end-to-end ranking classifier to simplify the architecture search process in multi-objective NAS, addressing the rank disorder issue and outperforming other methods.

Abstrak

In the content, the authors introduce a novel approach using a Pareto-wise ranking classifier to streamline multi-objective NAS. By transforming the complex task into a simple classification problem, they alleviate the rank disorder issue and achieve superior results compared to existing methods. The proposed method successfully identifies promising network architectures under various objectives and constraints.

Statistik

The proposed approach consumes only 1.19 GPU days on ImageNet.
CENAS-A achieves higher accuracy than manual design models with smaller #Params.
CENAS-C outperforms RL-based methods in terms of accuracy.

Kutipan

"The proposed approach is able to alleviate the rank disorder issue and outperforms other methods."
"The proposed method is able to find a set of promising network architectures with different model sizes ranging from 2M to 5M under diverse objectives and constraints."

Wawasan Utama Disaring Dari

Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search

by Lianbo Ma,Na... pada arxiv.org 03-12-2024

https://arxiv.org/pdf/2109.07582.pdf

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