NIRMAL Pooling: An Adaptive Max Pooling Approach with Non-linear Activation for Enhanced Image Classification
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912538184122368 |
|---|---|
| author | Gaud, Nirmal Jha, Krishna Kumar Adhikari, Jhimli S, Adhini Nasarin P Das, Joydeep Deshpande, Samarth S Barara, Nitasha Ramya, Vaduguru Venkata Saha, Santu Baran, Mehmet Tarik Venkateshwarlu, Sarangi D, Anusha M Mouli, Surej Katiyar, Preeti Chaudhary, Vipin Kumar |
| author_facet | Gaud, Nirmal Jha, Krishna Kumar Adhikari, Jhimli S, Adhini Nasarin P Das, Joydeep Deshpande, Samarth S Barara, Nitasha Ramya, Vaduguru Venkata Saha, Santu Baran, Mehmet Tarik Venkateshwarlu, Sarangi D, Anusha M Mouli, Surej Katiyar, Preeti Chaudhary, Vipin Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10940 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | NIRMAL Pooling: An Adaptive Max Pooling Approach with Non-linear Activation for Enhanced Image Classification Gaud, Nirmal Jha, Krishna Kumar Adhikari, Jhimli S, Adhini Nasarin P Das, Joydeep Deshpande, Samarth S Barara, Nitasha Ramya, Vaduguru Venkata Saha, Santu Baran, Mehmet Tarik Venkateshwarlu, Sarangi D, Anusha M Mouli, Surej Katiyar, Preeti Chaudhary, Vipin Kumar Computer Vision and Pattern Recognition 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. |
| title | NIRMAL Pooling: An Adaptive Max Pooling Approach with Non-linear Activation for Enhanced Image Classification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.10940 |