XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks

• URL: http://arxiv.org/abs/2312.07760v1
• Date: Tue, 12 Dec 2023 22:03:11 GMT
• Title: XC-NAS: A New Cellular Encoding Approach for Neural Architecture Search of Multi-path Convolutional Neural Networks
• Authors: Trevor Londt, Xiaoying Gao, Peter Andreae, Yi Mei
• Abstract summary: This paper introduces an algorithm capable of evolving novel multi-path CNN architectures of varying depth, width, and complexity for image and text classification tasks. By using a surrogate model approach, we show that the algorithm can evolve a performant CNN architecture in less than one GPU day. Experiment results show that the algorithm is highly competitive, defeating several state-of-the-art methods, and is generalisable to both the image and text domains.
• Score: 0.4915744683251149
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Convolutional Neural Networks (CNNs) continue to achieve great success in classification tasks as innovative techniques and complex multi-path architecture topologies are introduced. Neural Architecture Search (NAS) aims to automate the design of these complex architectures, reducing the need for costly manual design work by human experts. Cellular Encoding (CE) is an evolutionary computation technique which excels in constructing novel multi-path topologies of varying complexity and has recently been applied with NAS to evolve CNN architectures for various classification tasks. However, existing CE approaches have severe limitations. They are restricted to only one domain, only partially implement the theme of CE, or only focus on the micro-architecture search space. This paper introduces a new CE representation and algorithm capable of evolving novel multi-path CNN architectures of varying depth, width, and complexity for image and text classification tasks. The algorithm explicitly focuses on the macro-architecture search space. Furthermore, by using a surrogate model approach, we show that the algorithm can evolve a performant CNN architecture in less than one GPU day, thereby allowing a sufficient number of experiment runs to be conducted to achieve scientific robustness. Experiment results show that the approach is highly competitive, defeating several state-of-the-art methods, and is generalisable to both the image and text domains.

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