  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.
