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arXiv · 2508.10940

NIRMAL Pooling: An Adaptive Max Pooling Approach with Non-linear Activation for Enhanced Image Classification

Nirmal Gaud·Krishna Kumar Jha·Jhimli Adhikari·Adhini Nasarin P S·Joydeep Das·Samarth S Deshpande·Nitasha Barara·Vaduguru Venkata Ramya·Santu Saha·Mehmet Tarik Baran·Sarangi Venkateshwarlu·Anusha M D·Surej Mouli·Preeti Katiyar·Vipin Kumar Chaudhary
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Abstract

This paper presents NIRMAL Pooling, a novel pooling layer for Convolutional Neural Networks (CNNs) that integrates adaptive max pooling with non-linear activation function for image classification tasks. The acronym NIRMAL stands for Non-linear Activation, Intermediate Aggregation, Reduction, Maximum, Adaptive, and Localized. By dynamically adjusting pooling parameters based on desired output dimensions and applying a Rectified Linear Unit (ReLU) activation post-pooling, NIRMAL Pooling improves robustness and feature expressiveness. We evaluated its performance against standard Max Pooling on three benchmark datasets: MNIST Digits, MNIST Fashion, and CIFAR-10. NIRMAL Pooling achieves test accuracies of 99.25% (vs. 99.12% for Max Pooling) on MNIST Digits, 91.59% (vs. 91.44%) on MNIST Fashion, and 70.49% (vs. 68.87%) on CIFAR-10, demonstrating consistent improvements, particularly on complex datasets. This work highlights the potential of NIRMAL Pooling to enhance CNN performance in diverse image recognition tasks, offering a flexible and reliable alternative to traditional pooling methods.

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Nirmal Gaud, Krishna Kumar Jha, Jhimli Adhikari, Adhini Nasarin P S, Joydeep Das, Samarth S Deshpande, Nitasha Barara, Vaduguru Venkata Ramya, Santu Saha, Mehmet Tarik Baran, Sarangi Venkateshwarlu, Anusha M D, Surej Mouli, Preeti Katiyar, Vipin Kumar Chaudhary. 2025-08-13. NIRMAL Pooling: An Adaptive Max Pooling Approach with Non-linear Activation for Enhanced Image Classification. https://arxiv.org/abs/2508.10940

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