{"ID":2882734,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.10940","arxiv_id":"2508.10940","title":"NIRMAL Pooling: An Adaptive Max Pooling Approach with Non-linear Activation for Enhanced Image Classification","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.","short_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 Loc...","url_abs":"https://arxiv.org/abs/2508.10940","url_pdf":"https://arxiv.org/pdf/2508.10940v1","authors":"[\"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\"]","published":"2025-08-13T10:18:49Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}