Deep Convolutional Neural Network (CNN) is a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing. Some of the exciting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, and Speech Recognition. The powerful learning ability of deep CNN is primarily due to the use of multiple feature extraction stages that can automatically learn representations from the data. The availability of a large amount of data and improvement in the hardware technology has accelerated the research in CNNs, and recently interesting deep CNN architectures have been reported. Several inspiring ideas to bring advancements in CNNs have been explored, such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the significant improvement in the representational capacity of the deep CNN is achieved through architectural innovations. Notably, the ideas of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing have gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and, consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges, and applications of CNN are also provided.

中文翻译：

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深度卷积神经网络近期架构综述

深度卷积神经网络 (CNN) 是一种特殊类型的神经网络，在与计算机视觉和图像处理相关的多项比赛中表现出色。CNN 的一些令人兴奋的应用领域包括图像分类和分割、对象检测、视频处理、自然语言处理和语音识别。深度 CNN 强大的学习能力主要是由于使用了多个特征提取阶段，可以自动从数据中学习表示。大量数据的可用性和硬件技术的改进加速了 CNN 的研究，最近报道了有趣的深度 CNN 架构。已经探索了几个鼓舞人心的想法来推动 CNNs 的进步，例如使用不同的激活和损失函数、参数优化、正则化和架构创新。然而，深度CNN的表征能力的显着提升是通过架构创新实现的。值得注意的是，利用空间和通道信息、架构的深度和宽度以及多路径信息处理的思想得到了广泛的关注。同样，使用层块作为结构单元的想法也越来越流行。因此，本次调查侧重于最近报道的深度 CNN 架构中存在的内在分类法，因此，将 CNN 架构的最新创新分为七个不同的类别。这七类基于空间开发、深度、多路径、宽度、特征图开发、渠道提升和注意力。此外，还提供了对 CNN 组件、当前挑战和 CNN 应用的基本理解。