Rethinking Pooling: Learnable Aggregations for Robust and Efficient Feature Extraction

Pooling operations are a fundamental component of convolutional neural networks (CNNs), enabling spatial invariance and reducing computational complexity. However, traditional pooling methods, such as max pooling and average pooling, are fixed and do not adapt to the specific features being aggregated. This paper proposes a novel approach to pooling, termed "Learnable Aggregations," which introduces trainable parameters to dynamically weight and combine feature activations within the pooling region. We explore various learnable aggregation functions, including attention-based mechanisms and parameterized linear combinations. We demonstrate that these learnable pooling strategies can significantly improve the robustness and efficiency of feature extraction across a range of computer vision tasks, including image classification, object detection, and semantic segmentation. Our experiments show that learnable aggregations can achieve higher accuracy and faster convergence compared to traditional pooling methods, while also providing greater flexibility in adapting to diverse data distributions and network architectures. The proposed methods offer a promising direction for rethinking pooling operations in deep learning and enhancing the performance of CNNs.