Abstract

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.

Affiliated Institutions

Related Publications

Squeeze-and-Excitation Networks

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial a...

2019 IEEE Transactions on Pattern Analysis... 12023 citations

Squeeze-and-Excitation Networks

Convolutional neural networks are built upon the convolution operation, which extracts informative features by fusing spatial and channel-wise information together within local ...

2018 25361 citations

Publication Info

Year
2020
Type
article
Volume
53
Issue
8
Pages
5455-5516
Citations
2224
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

2224
OpenAlex
97
Influential

Cite This

Asifullah Khan, Anabia Sohail, Umme Zahoora et al. (2020). A survey of the recent architectures of deep convolutional neural networks. Artificial Intelligence Review , 53 (8) , 5455-5516. https://doi.org/10.1007/s10462-020-09825-6

Identifiers

DOI
10.1007/s10462-020-09825-6
arXiv
1901.06032

Data Quality

Data completeness: 79%