Abstract
Deep Convolutional Neural Networks (CNNs) have significantly advanced deep learning, driving breakthroughs in computer vision, natural language processing, medical diagnosis, object detection, and speech recognition. Architectural innovations including 1D, 2D, and 3D convolutional models, dilated and grouped convolutions, depthwise separable convolutions, and attention mechanisms address domain-specific challenges and enhance feature representation and computational efficiency. Structural refinements such as spatial-channel exploitation, multi-path design, and feature-map enhancement contribute to robust hierarchical feature extraction and improved generalization, particularly through transfer learning. Efficient preprocessing strategies, including Fourier transforms, structured transforms, low-precision computation, and weight compression, optimize inference speed and facilitate deployment in resource-constrained environments. This survey presents a unified taxonomy that classifies CNN architectures based on spatial exploitation, multi-path structures, depth, width, dimensionality expansion, channel boosting, and attention mechanisms. It systematically reviews CNN applications in face recognition, pose estimation, action recognition, text classification, statistical language modeling, disease diagnosis, radiological analysis, cryptocurrency sentiment prediction, 1D data processing, video analysis, and speech recognition. In addition to consolidating architectural advancements, the review highlights emerging learning paradigms such as few-shot, zero-shot, weakly supervised, federated learning frameworks and future research directions include hybrid CNN-transformer models, vision-language integration, generative learning, etc. This review provides a comprehensive perspective on CNN's evolution from 2015 to 2025, outlining key innovations, challenges, and opportunities.
Abstract (translated)
深度卷积神经网络（CNNs）在深度学习领域取得了重大进展，推动了计算机视觉、自然语言处理、医学诊断、目标检测和语音识别等领域的突破。包括一维、二维及三维卷积模型、扩张卷积与分组卷积、逐深可分离卷积以及注意力机制在内的架构创新解决了特定领域的挑战，并增强了特征表示能力及计算效率。空间-通道利用、多路径设计及特征图增强等结构改进有助于稳健的层级特征提取和泛化性能提升，特别是在迁移学习中表现尤为突出。有效的预处理策略，如傅里叶变换、结构转换、低精度运算及权重压缩，则优化了推理速度并支持在资源受限环境中部署应用。 本文综述提出了一种统一分类体系，根据空间利用、多路径架构、深度、宽度、维度扩展、通道增强和注意力机制对CNN架构进行分类。系统性地回顾了CNN在人脸识别、姿态估计、动作识别、文本分类、统计语言建模、疾病诊断、放射学分析、加密货币情感预测、一维数据处理、视频分析及语音识别等领域的应用。除了总结结构上的进步，还强调了新兴的学习范式如小样本学习（few-shot learning）、零样本学习（zero-shot learning）、弱监督学习和联邦学习框架，并展望了未来研究方向包括混合CNN-Transformer模型、视觉语言整合以及生成性学习等。 本文提供了2015年至2025年间CNN进化历程的全面视角，概述了关键创新、挑战及机遇。
URL
https://arxiv.org/abs/2503.16546
