Squeeze-and-Excitation Networks (1709.01507v4)

Abstract: The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251%, surpassing the winning entry of 2016 by a relative improvement of ~25%. Models and code are available at https://github.com/hujie-frank/SENet.

• The paper introduces the novel SE block, which recalibrates channel-wise feature responses using global pooling and a gating mechanism.
• The paper shows that integrating SE blocks into CNNs, like ResNet-50, significantly improves accuracy while adding only a modest computational overhead.
• The paper demonstrates that SE blocks generalize across various architectures and tasks, paving the way for dynamic and efficient network designs.

Squeeze-and-Excitation Networks: Enhancing CNN Feature Representation via Channel-Wise Attention

The paper "Squeeze-and-Excitation Networks" by Jie Hu et al., provides a detailed paper of a novel architectural unit called the Squeeze-and-Excitation (SE) block, which is designed to improve the representational power of convolutional neural networks (CNNs). SE blocks enable networks to recalibrate channel-wise feature responses dynamically, exploiting global information to highlight useful features and suppress redundant ones.

Core Contribution

The principal contribution of the paper is the introduction of the SE block. Unlike traditional convolution operations that fuse spatial and channel-wise information in a local manner, SE blocks focus explicitly on modeling interdependencies between channels. The SE block is composed of two main operations: squeeze and excitation.

1. Squeeze: This operation globalizes spatial information using global average pooling, resulting in a descriptor capturing channel-wise statistics.
2. Excitation: Following the squeeze operation, a simple gating mechanism (using fully connected layers and a sigmoid activation) generates a set of modulation weights, which are then applied to recalibrate the feature maps.

Performance Insights

One of the significant empirical outcomes of integrating SE blocks into various architectures (e.g., ResNet, Inception) is the substantial improvement in performance with minimal additional computational cost. For instance, when SE blocks were integrated into ResNet-50, the resultant SE-ResNet-50 achieved a top-5 error rate of 6.62% on ImageNet, significantly outperforming the baseline ResNet-50 (7.48%) and approaching the deeper ResNet-101 model (6.52%).

Theoretical and Practical Implications

Theoretically, SE blocks extend the representational power of CNNs by providing a mechanism to emphasize informative features dynamically. This enhancement is particularly important as it allows networks to adaptively weigh feature maps, thus improving the network's ability to focus on salient features relevant to the specific task. Practically, SE blocks offer a significant performance boost with a modest increase in model complexity (e.g., a 10% parameter increase in SE-ResNet-50).

Additionally, SE blocks exhibit versatility across various network architectures and datasets, demonstrating their generalizability. For example, the SE block was successfully integrated into models like ResNeXt, Inception-ResNet, MobileNet, and ShuffleNet, consistently improving accuracy across different vision tasks, including object detection and scene classification.

Bold Claims and Evaluations

A notable bold claim made in the paper is that SE blocks can outperform deeper networks in certain configurations. This is evidenced by SE-ResNet-50's performance, which matches that of deeper architectures like ResNet-101. The paper supports these claims with extensive experiments, showing that SE blocks yield improvements not just on ImageNet but also on CIFAR-10, CIFAR-100, and the COCO dataset for object detection.

Future Developments in AI

The introduction of SE blocks opens doors to several avenues for future developments in AI:

1. Architecture Design: The methodology of channel-wise recalibration through SE blocks can inspire new designs in neural network architectures that can further exploit inter-channel dependencies.
2. Efficient Model Design: The SE block provides insights into designing models that are both computationally efficient and powerful, crucial for deploying AI models on resource-constrained devices.
3. Dynamic Networks: SE blocks' dynamic nature, which adapts to input-specific characteristics, aligns well with the trend towards adaptive and context-aware neural networks.

Conclusion

In summary, the paper presents SE blocks as a nuanced advancement in CNN architecture design, focusing on the often-underexplored channel relationships in convolutional features. Through demonstrated empirical results, the authors establish that SE blocks can significantly enhance the performance of various state-of-the-art networks while maintaining computational efficiency. This work posits that the recalibration of feature maps via global context information is a potent approach to enriching feature representation and serving broader AI research and applications.