Squeeze and Excitation

Squeeze-and-excitation (SE) networks are a lightweight yet powerful technique used to improve the performance of deep learning models by recalibrating channel-wise feature responses. Current research focuses on integrating SE blocks into various architectures for diverse applications, including speech processing (e.g., enhancement, emotion recognition, language identification), sound event detection, and medical image analysis (e.g., brain tumor segmentation). This approach enhances feature representation learning, leading to improved accuracy and efficiency, particularly in resource-constrained scenarios or when dealing with limited training data. The widespread adoption of SE networks across multiple domains highlights their significant impact on improving the performance and efficiency of deep learning models.