Separable Convolution

Depthwise separable convolutions are a computationally efficient modification of standard convolutional layers, primarily aimed at reducing the number of parameters and operations in deep neural networks while maintaining comparable accuracy. Current research focuses on integrating this technique into various architectures, including U-Nets, GANs, and Vision Transformers, for applications ranging from image fusion and segmentation to object detection and speech processing. This efficiency boost is particularly significant for deploying deep learning models on resource-constrained devices like embedded systems and mobile platforms, impacting diverse fields from medical imaging to remote sensing.