Large Receptive Field

Large receptive fields in deep learning models aim to enhance the network's ability to capture contextual information and long-range dependencies within input data, improving performance on tasks like image segmentation, object detection, and super-resolution. Current research focuses on efficiently expanding receptive fields without excessive computational cost, employing techniques such as wavelet transforms, large kernel convolutions, and novel attention mechanisms within architectures like Transformers and CNNs. These advancements lead to improved accuracy and efficiency in various applications, particularly in medical image analysis and computer vision, where capturing global context is crucial for accurate interpretation.