FlexPooling with Simple Auxiliary Classifiers in Deep Networks

2026-06-12

AI Summary

This work addresses the limitation of conventional pooling operations—such as max and average pooling—in discarding discriminative information during downsampling. To mitigate this issue, the authors propose FlexPooling, an adaptive pooling mechanism that generalizes average pooling into a learnable weighted formulation, optimized end-to-end alongside the main network. A lightweight Simple Auxiliary Classifier (SAC) is further introduced to collaboratively guide the learning of pooling weights, thereby enhancing the preservation of salient features. Experimental results demonstrate that FlexPooling consistently improves model accuracy by 1%–3% across multiple image classification benchmarks, significantly outperforming baseline pooling strategies.

Abstract

In computer vision, the basic pipeline of most convolutional neural networks consists of multiple feature extraction layers, where the input signal is downsampled to a lower resolution in each subsequent layer. This downsampling process is commonly referred to as pooling, which is an essential operation in CNNs. Pooling improves robustness against transformations, reduces the number of trainable parameters, increases the receptive field, and lowers computation time. Since pooling is a lossy process but remains important for extracting high-level information from low-level representations, it is important to preserve the most prominent information from previous activations to improve network discriminability. Standard pooling is usually performed using dense pooling methods, such as max pooling or average pooling, or through strided convolutional kernels. In this paper, we propose a simple yet effective adaptive pooling method, called FlexPooling, which generalizes average pooling by learning a weighted average over activations jointly with the rest of the network. We further show that attaching Simple Auxiliary Classifiers (SAC) to the CNN improves performance and demonstrates the effectiveness of the proposed method compared with standard pooling methods. Experiments on multiple popular image classification datasets show that FlexPooling consistently outperforms baseline networks, achieving approximately 1 to 3 percent improvement in accuracy.

Problem

• pooling
• convolutional neural networks
• feature extraction
• discriminability
• downsampling

Innovation

• FlexPooling
• adaptive pooling
• weighted average
• auxiliary classifiers
• convolutional neural networks