Multi Scale Residual

Multi-scale residual networks leverage the power of convolutional neural networks by processing information at multiple resolutions simultaneously, aiming to improve feature extraction and model robustness. Current research focuses on integrating multi-scale residual blocks into diverse architectures, including U-Nets, transformers, and recurrent networks, for applications ranging from image compression and video processing to medical image segmentation and anomaly detection. This approach enhances performance in various tasks by capturing both fine-grained details and broader contextual information, leading to improved accuracy and efficiency in numerous fields. The resulting models often demonstrate superior performance compared to single-scale approaches, particularly in complex or noisy data.