Dilated Convolutional Neural Network

Dilated convolutional neural networks (Dilated CNNs) are a class of deep learning models designed to efficiently process sequential or spatial data with long-range dependencies, addressing limitations of standard convolutional networks. Current research focuses on enhancing Dilated CNN architectures through techniques like multi-scale feature extraction, attention mechanisms, and hybrid models incorporating recurrent networks or transformers, achieving improved performance in diverse applications. These advancements are significantly impacting fields such as time series forecasting, medical image analysis (e.g., ECG analysis, polyp segmentation), and speech processing, enabling more accurate and efficient solutions for complex tasks.