Dilated Convolution

Dilated convolutions are a modified convolutional operation that expands the receptive field of a neural network without increasing the number of parameters, enabling the capture of broader contextual information within images or time series. Current research focuses on integrating dilated convolutions into various architectures, such as U-Nets, ResNets, and Transformers, often combined with techniques like attention mechanisms and learnable spacings to improve performance in tasks ranging from image segmentation and object detection to audio classification and medical image analysis. This technique's effectiveness in enhancing model efficiency and accuracy across diverse applications makes it a significant advancement in deep learning, particularly for resource-constrained environments and high-resolution data processing.