Pooling Layer

Pooling layers are crucial components in neural networks, aiming to reduce computational cost and improve model generalization by summarizing information from multiple inputs. Current research focuses on developing more sophisticated pooling methods tailored to specific data types (e.g., graphs, point clouds, time series) and model architectures (e.g., transformers, convolutional networks), often incorporating adaptive or learned pooling strategies to avoid information loss. These advancements are improving performance in diverse applications, including image classification, object detection, natural language processing, and graph analysis, by enabling more efficient and effective feature extraction.