A Survey of the Recent Architectures of Deep Convolutional Neural Networks (1901.06032v7)

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.

Overview of Recent Architectures of Deep Convolutional Neural Networks

The paper "A Survey of the Recent Architectures of Deep Convolutional Neural Networks" by Asifullah Khan, Anabia Sohail, Umme Zahoora, and Aqsa Saeed Qureshi provides a comprehensive review of innovative architectural advancements in deep Convolutional Neural Networks (CNNs). The paper focuses on how various modifications and transformations in CNN architectures contribute to increased representational capacities and performance in numerous computer vision tasks.

Key Contributions

The manuscript delineates the taxonomy of CNNs into seven distinct categories based on architectural innovation:

1. Spatial Exploitation
2. Depth
3. Multi-Path
4. Width
5. Feature-Map Exploitation
6. Channel Boosting
7. Attention

Spatial Exploitation Architectures

The spatial exploitation category encompasses architectures like LeNet, AlexNet, ZfNet, VGG, and GoogleNet, which primarily optimize the spatial relations between the input data through various filter sizes and strides. AlexNet's utilization of ReLU and dropout, ZfNet's layer visualization, VGG's homogenous topology, and GoogleNet’s Inception modules for multi-scale feature extraction, all exhibit significant advancements in handling input data at different spatial resolutions.

Depth-Based Architectures

The depth category comprises architectures such as Inception V3, V4, Inception-ResNet, and ResNet, underlining the importance of deeper networks in harnessing richer feature hierarchies. These architectures tackle the vanishing gradient problem and improve the generalization of deep networks through enhanced depth and architectural modules like residual blocks.

Multi-Path Architectures

The multi-path category includes highway networks, ResNet, DenseNet, and other architectures that introduce cross-layer connectivity to address gradient diminishing issues in deep networks. This multi-path approach allows an unimpeded flow of information through various layers, enhancing training efficiency and convergence.

Width-Based Architectures

The width category is illustrated by models such as Wide ResNet, ResNeXt, and PyramidalNet, which advocate increasing the width rather than the depth of networks to enhance representational power. The introduction of cardinality in ResNeXt and gradual increase in feature map depth in PyramidalNet are pivotal in demonstrating how enhanced network width can significantly improve performance.

Feature-Map Exploitation Architectures

In the feature-map exploitation category, architectures like Squeeze-and-Excitation Networks focus on channel-wise feature recalibration to emphasize important features and suppress irrelevant ones. This technique has led to substantial error reduction and improved model performance on various benchmark datasets.

Channel Boosting Architectures

The channel boosting category introduces architectures such as Channel Boosted CNNs, which augment the input channels with artificially generated channels through auxiliary learners. This approach leverages transfer learning to provide a richer and more diverse input representation, thereby enhancing model performance.

Attention-Based Architectures

The attention category highlights architectures like Residual Attention Networks (RAN) and Convolutional Block Attention Modules (CBAM) which incorporate attention mechanisms to focus on essential parts of the input data. These architectures adaptively assign importance to various features, improving the network's ability to handle complex scenes and cluttered backgrounds.

Practical and Theoretical Implications

The survey underscores the significant practical and theoretical implications of architectural innovations in CNNs. Practically, these architectures have revolutionized fields such as image classification, object detection, segmentation, and various other computer vision tasks. Theoretically, the refinement and modification of CNN structures have laid a robust foundation for future research and development in deep learning architectures.

Future Directions

The paper posits that future research may explore ensemble learning, generative modeling, advanced attention mechanisms, cloud-based platforms, and hardware accelerators to further the efficiency and applicability of CNNs. Innovations in these areas could pave the way for more robust, scalable, and versatile CNN architectures.

Conclusion

Overall, this survey articulates the evolution and trends in CNN architectures, emphasizing the critical role of innovative structural designs in enhancing machine learning capabilities. Continued exploration and refinement in this domain are likely to yield even more powerful and efficient models, pushing the boundaries of what is achievable in artificial intelligence and machine vision tasks.