A Deep Learning-Based Buffalo Optimizer based Squeeze and Excitation Network for Garbage Classification for a Sustainable Environment
Solid Waste Management
A Squeeze and Excitation Network is a deep-learning architectural component designed to enhance networks. The "squeeze" step reduces the spatial dimensions of the input feature maps, and the "excitation" step adaptively recalibrates channel-wise feature responses. This allows the network to focus on...