Squeeze Excitation

Squeeze-and-excitation (SE) networks are a class of deep learning architectures designed to improve feature representation by recalibrating channel-wise feature responses. Current research focuses on integrating SE blocks into various models, including convolutional neural networks (CNNs), UNets, and capsule networks, for applications ranging from image classification and segmentation to natural language processing and signal processing. This attention mechanism enhances feature extraction by selectively weighting different channels based on their importance, leading to improved performance in diverse tasks such as medical image analysis, sound event detection, and activity recognition. The widespread adoption of SE networks highlights their effectiveness in boosting the performance of existing deep learning models across numerous domains.