AdaNet: A competitive adaptive convolutional neural network for spectral information identification

Abstract

Spectral analysis-based non-destructive testing techniques can monitor food authenticity, quality changes, and traceability. Convolutional neural networks (CNNs) are widely used for spectral information processing and decision-making because they can effectively extract features from spectral data. However, CNNs introduce redundancy in feature extraction, thereby wasting computational resources. This paper proposes a competitive adaptive CNN (AdaNet) to address these challenges. First, adaptive convolution (AdaConv) is used to select spectral features based on channel attention and optimize computational resource allocation. Second, a Gaussian-initialized parameter matrix is applied to rescale spatial relationships and reduce redundancy. Finally, a self-attention mask is employed to mitigate the information loss due to convolution and speed up the convergence of AdaConv. We evaluate AdaNet’s performance compared to other advanced methods. The results show that AdaNet outperforms state-of-the-art techniques, achieving average accuracies of 99.10% and 98.50% on datasets 1 and 2, respectively. We provide a viable approach to enhance the engineering applications of spectral analysis techniques. Code is available at https://github.com/Ziyang-Li-AILab/AdaNet.