Wavelet-integrated deep neural networks: A systematic review of applications and synergistic architectures

IF 6.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jiangtao Wu, Jiaqi Li, Jie Yang, Shuli Mei
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引用次数: 0

Abstract

Wavelet transforms, known for their exceptional capabilities in multi-resolution analysis, have garnered significant attention in the integration with deep neural networks to address key challenges in complex pattern analysis and recognition tasks. This review examines how the integration of wavelet transforms with emerging deep learning techniques has accelerated progress across various domains, such as image and video processing, graph and spatial-temporal data analysis. By integrating wavelets into traditional deep learning models, such as convolutional neural networks (CNNs), and emerging architectures like transformers and diffusion models, we show how these hybrid methods improve multi-scale feature representation, efficiency, and interpretability, while mitigating common deep learning limitations such as high computational costs and reduced robustness in multi-resolution analysis. We systematically address the synergy between wavelet transforms and deep learning, a topic underexplored in previous literature, and highlight the diverse strategies of wavelet integration—ranging from foundational methods to advanced neural network architectures—and conduct a comparative analysis of their performance in real-world applications. We also identify critical gaps and present directions for future research, particularly in the areas of adaptive, data-driven wavelet frameworks and their potential in generative modeling and domain adaptation.
小波集成深度神经网络:应用和协同架构的系统回顾
小波变换以其在多分辨率分析中的卓越能力而闻名,在与深度神经网络的集成中,以解决复杂模式分析和识别任务中的关键挑战,引起了人们的极大关注。这篇综述探讨了小波变换与新兴深度学习技术的集成如何加速了图像和视频处理、图形和时空数据分析等各个领域的进展。通过将小波集成到传统的深度学习模型中,如卷积神经网络(cnn),以及变压器和扩散模型等新兴架构,我们展示了这些混合方法如何改善多尺度特征表示、效率和可解释性,同时减轻了常见的深度学习限制,如高计算成本和多分辨率分析中的鲁棒性降低。我们系统地讨论了小波变换和深度学习之间的协同作用,这是以前文献中未充分探讨的主题,并强调了小波集成的各种策略——从基本方法到高级神经网络架构——并对它们在实际应用中的性能进行了比较分析。我们还确定了未来研究的关键差距和方向,特别是在自适应、数据驱动的小波框架及其在生成建模和领域适应中的潜力方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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