Residual Convolutional Neural Network

Residual Convolutional Neural Networks (ResNets) are a class of deep learning models designed to overcome the vanishing gradient problem in training very deep networks, enabling the learning of complex features from data. Current research focuses on enhancing ResNets through techniques like attention mechanisms, dilated convolutions, and hybrid architectures incorporating transformers, improving performance in diverse applications such as medical image analysis (e.g., skin lesion segmentation, brain injury diagnosis, chest infection classification) and signal processing (e.g., acoustic mosquito identification, ocean temperature prediction). The effectiveness and relative efficiency of ResNets across various tasks, coupled with ongoing efforts to improve their interpretability and robustness, makes them a significant tool in numerous scientific and practical domains.