What does this research mean for the field?

Alpha-controlled Adaptive Pooling improves the stability and effectiveness of convolutional neural networks in image classification tasks, achieving competitive accuracy compared to conventional pooling methods. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The research aims to improve pooling techniques in CNNs by introducing an alpha-controlled adaptive pooling method for image classification tasks.

March 2, 2026Open Access

Improving Convolutional Neural Network Performance Using Alpha-Based Adaptive Pooling for Image Classification

Key Points

The research aims to improve pooling techniques in CNNs by introducing an alpha-controlled adaptive pooling method for image classification tasks.
Introduced an alpha parameter for pooling adjustments ranging from 0 to 1.
Evaluated the Adaptive Pooling method using MNIST and CIFAR-10 datasets.
Conducted experiments across different CNN architectures: LeNet-5, custom CNN, and ResNet-18.
Adaptive Pooling achieved 87.2% on MNIST and 30.1% on CIFAR-10 with LeNet-5.
Under optimal settings, the custom CNN reached 99.7% on MNIST and 86.1% on CIFAR-10.
ResNet-18 achieved 99.1% on MNIST but 37.2% on CIFAR-10, indicating architecture-dependent performance.

Abstract

This study proposes an Adaptive Pooling method based on an alpha (α) parameter to enhance the effectiveness and stability of convolutional neural networks (CNNs) in image classification tasks. Conventional pooling techniques, such as max pooling and average pooling, often exhibit limited adaptability when applied to datasets with heterogeneous distributions and varying levels of complexity. To address this limitation, the proposed approach introduces an α parameter ranging from 0 to 1 that continuously regulates the contribution of maximum-based and average-based pooling operations in a unified and flexible framework. The proposed method is evaluated using two benchmark datasets, MNIST and CIFAR-10, representing grayscale and color image classification scenarios, respectively. Experiments are conducted across three CNN families with different depths LeNet-5, a deeper custom-built CNN, and ResNet-18 to assess robustness under varying representational capacity. Under the best α setting with a 4 × 4 pooling configuration, Adaptive Pooling exhibits architecture-dependent behavior. On LeNet-5, Adaptive Pooling achieves 87.2% on MNIST and 30.1% on CIFAR-10, compared with 97.8% (max/average pooling) on MNIST and 60.1% (max pooling)/53.9% (average pooling) on CIFAR-10. In contrast, on the deeper custom CNN, Adaptive Pooling becomes competitive, reaching 99.7% on MNIST and 86.1% on CIFAR-10, which is comparable to 99.6%–99.7% on MNIST and 84.5%–86.2% on CIFAR-10 achieved by conventional pooling. On ResNet-18, Adaptive Pooling attains 99.1% on MNIST, while CIFAR-10 performance decreases to 37.2% relative to the default global average pooling baseline (99.7% on MNIST and 89.0% on CIFAR-10), suggesting that performance also depends on where the pooling replacement is applied. Overall, these findings indicate that α-controlled Adaptive Pooling provides a lightweight and configurable pooling strategy that can improve stability and achieve competitive accuracy in deeper CNNs, although it should be treated as a complementary mechanism rather than a universal replacement across all architectures.

Bookmark

View Full Paper

Cite This Study

Saubari et al. (Thu,) studied this question.

synapsesocial.com/papers/69a52dbff1e85e5c73bf0c75 https://doi.org/https://doi.org/10.32604/cmc.2026.077087

Bookmark

View Full Paper