Residual Neural Network

Residual Neural Networks (ResNets) are deep learning architectures designed to overcome the vanishing gradient problem in training very deep networks, enabling the efficient learning of complex patterns from data. Current research focuses on improving ResNet performance through adaptive weight adjustments, iterative refinement techniques, and optimized architectures tailored to specific data types (e.g., time-series data, images, point clouds). These advancements are impacting diverse fields, including image processing, natural language processing, medical image analysis, and game playing, by enabling the development of more accurate and efficient models for various tasks.