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

• URL: http://arxiv.org/abs/2109.07582v2
• Date: Sat, 9 Mar 2024 00:01:32 GMT
• Title: Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search
• Authors: Lianbo Ma, Nan Li, Guo Yu, Xiaoyu Geng, Min Huang and Xingwei Wang
• Abstract summary: This study focuses on how to find feasible deep models under diverse design objectives. We propose a classification-wise Pareto evolution approach for one-shot NAS, where an online classifier is trained to predict the dominance relationship between the candidate and constructed reference architectures. We find a number of neural architectures with different model sizes ranging from 2M to 6M under diverse objectives and constraints.
• Score: 15.454709248397208
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the deployment of deep neural models, how to effectively and automatically find feasible deep models under diverse design objectives is fundamental. Most existing neural architecture search (NAS) methods utilize surrogates to predict the detailed performance (e.g., accuracy and model size) of a candidate architecture during the search, which however is complicated and inefficient. In contrast, we aim to learn an efficient Pareto classifier to simplify the search process of NAS by transforming the complex multi-objective NAS task into a simple Pareto-dominance classification task. To this end, we propose a classification-wise Pareto evolution approach for one-shot NAS, where an online classifier is trained to predict the dominance relationship between the candidate and constructed reference architectures, instead of using surrogates to fit the objective functions. The main contribution of this study is to change supernet adaption into a Pareto classifier. Besides, we design two adaptive schemes to select the reference set of architectures for constructing classification boundary and regulate the rate of positive samples over negative ones, respectively. We compare the proposed evolution approach with state-of-the-art approaches on widely-used benchmark datasets, and experimental results indicate that the proposed approach outperforms other approaches and have found a number of neural architectures with different model sizes ranging from 2M to 6M under diverse objectives and constraints.

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