Global Pooling

Global pooling is a crucial operation in deep learning, aggregating feature information from spatial or temporal dimensions into a compact representation for classification or other downstream tasks. Current research focuses on developing more sophisticated pooling methods beyond simple averaging, including learnable weighted sums, optimal transport-based approaches, and those incorporating group symmetries or designed for specific data structures like point clouds. These advancements aim to improve model performance, particularly in scenarios with limited data or complex data structures, and enhance the interpretability and generalizability of deep learning models across various applications.