Depth Wise Convolution

Depthwise convolution is a specialized convolutional operation that applies a single filter to each input channel independently, significantly reducing computational cost compared to standard convolutions. Current research focuses on integrating depthwise convolutions into various architectures, including CNNs, Vision Transformers, and hybrid models, to improve efficiency while maintaining or enhancing accuracy in tasks like image classification, object detection, and segmentation. This technique's impact is seen in the development of lightweight, faster models suitable for resource-constrained environments (e.g., mobile devices, edge computing) and in improving the efficiency of larger models through pruning and other optimization strategies.