# A Survey of the Recent Architectures of Deep Convolutional Neural Networks

## TL;DR

This survey reviews recent deep CNN architectures, categorizing innovations into seven types, and discusses their components, challenges, and applications in computer vision and beyond.

## Contribution

It provides a comprehensive taxonomy of recent CNN architectural innovations, aiding understanding and guiding future research directions.

## Key findings

- Classifies CNN innovations into seven categories
- Highlights architectural strategies like multi-path and attention
- Summarizes CNN components, challenges, and applications

## 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.

---
Source: https://tomesphere.com/paper/1901.06032