Squeeze-and-Excitation Networks

Squeeze-and-Excitation Networks

16 May 2019 | Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu
The paper introduces the Squeeze-and-Excitation (SE) block, a novel architectural unit designed to enhance the representational power of convolutional neural networks (CNNs) by explicitly modeling channel-wise feature dependencies. The SE block consists of two main operations: *squeeze* and *excitation*. The *squeeze* operation reduces the feature maps to a channel descriptor, capturing global information about the channel-wise feature responses. The *excitation* operation then uses this descriptor to generate modulation weights, which are applied to the feature maps to selectively emphasize informative features and suppress less useful ones. The SE block can be stacked to form SENet architectures, which have shown significant improvements in performance across various datasets and tasks, including image classification, scene classification, and object detection. The authors demonstrate that SE blocks can be integrated into existing state-of-the-art architectures with minimal computational overhead and achieve top-5 error rates comparable to or better than those of deeper networks. The paper also includes ablation studies to validate the effectiveness of different components of the SE block and provides insights into the role of each operation in the network.The paper introduces the Squeeze-and-Excitation (SE) block, a novel architectural unit designed to enhance the representational power of convolutional neural networks (CNNs) by explicitly modeling channel-wise feature dependencies. The SE block consists of two main operations: *squeeze* and *excitation*. The *squeeze* operation reduces the feature maps to a channel descriptor, capturing global information about the channel-wise feature responses. The *excitation* operation then uses this descriptor to generate modulation weights, which are applied to the feature maps to selectively emphasize informative features and suppress less useful ones. The SE block can be stacked to form SENet architectures, which have shown significant improvements in performance across various datasets and tasks, including image classification, scene classification, and object detection. The authors demonstrate that SE blocks can be integrated into existing state-of-the-art architectures with minimal computational overhead and achieve top-5 error rates comparable to or better than those of deeper networks. The paper also includes ablation studies to validate the effectiveness of different components of the SE block and provides insights into the role of each operation in the network.
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