Receptive Field

A receptive field, in the context of neural networks, defines the region of input data that influences the output of a single neuron or feature map. Current research focuses on expanding receptive fields to improve the ability of models (like U-Nets, Transformers, and Mamba-based architectures) to capture long-range dependencies and contextual information, particularly in image segmentation and time series forecasting. This is achieved through techniques such as dilated convolutions, attention mechanisms, and novel scanning strategies, ultimately aiming for improved accuracy and efficiency in various applications, including medical image analysis and remote sensing. The impact of receptive field size on model performance and generalization is a key area of investigation, with a growing emphasis on balancing computational cost with the benefits of broader contextual understanding.