An Adaptive Differentiable Neural Network Architecture Search Algorithm and Its Application for sewer defect detection

Shaomiao Chen (), Ao Bai, Zhiwen Lei, Liming Jiang (), Dacheng He (), Kuanching Li () and Wei Liang ()
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Shaomiao Chen: Hunan University of Science and Technology
Ao Bai: Hunan University of Science and Technology
Zhiwen Lei: Hunan University of Science and Technology
Liming Jiang: Hunan University of Science and Technology
Dacheng He: Hunan University of Science and Technology
Kuanching Li: Hunan University of Science and Technology
Wei Liang: Hunan University of Science and Technology

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 3, No 21, 9 pages

Abstract: Abstract Differentiable neural network architecture search is now a popular way to automatically build deep networks in the past few years, thanks to its ease of implementation and efficiency. However, gradient-based search methods within differentiable spaces often suffer from search bias, particularly a tendency to favor skip connections, which can lead to suboptimal network performance. Although existing studies attempt to mitigate this issue by improving the exploration capacity of the algorithm, these approaches often incur significant computational overhead. In this work, we propose an adaptive exploration-based differentiable neural network architecture search algorithm, named AE-DARTS. AE-DARTS improves the effectiveness of algorithm exploration from both search and computation perspectives. In the search-direction-oriented approach, a composite interference term is introduced to enable both effectiveness and randomness in exploration. On the computational side, a dynamic partial edge sampling search strategy is proposed to reduce exploration costs and improve the exploitation precision. Furthermore, we have reimagined the cell structure to satisfy the lightweight demands of the AE-DARTS application for detecting sewer defects. Experiments show that the proposed method can find neural network architectures with higher feature representation ability with the same or even less computational overhead than current differentiable neural architecture search methods.

Keywords: Deep learning; Neural architecture search; Differentiable architecture search; Adaptive exploration; Sewer defect detection (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11235-025-01329-4

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