@article{Byeon2026, 
author = {Seunggyu Byeon and Jung-hun Lee and Jong-Deok Kim},
title = {Fuzzy C-Means Clustering-Driven Pooling for Robust and Generalizable Convolutional Neural Networks},
year = {2026},
journal = {Computers, Materials & Continua},
volume = {87},
number = {2},
pages = {24},
keywords = {Fuzzy logic, fuzzy c-means clustering, membership-based pooling, convolutional neural networks, downsampling, feature extraction},
url = {https://www.sciopen.com/article/10.32604/cmc.2025.074033},
doi = {10.32604/cmc.2025.074033},
abstract = {This paper introduces a fuzzy C-means-based pooling layer for convolutional neural networks that explicitly models local uncertainty and ambiguity. Conventional pooling operations, such as max and average, apply rigid aggregation and often discard fine-grained boundary information. In contrast, our method computes soft memberships within each receptive field and aggregates cluster-wise responses through membership-weighted pooling, thereby preserving informative structure while reducing dimensionality. Being differentiable, the proposed layer operates as standard two-dimensional pooling. We evaluate our approach across various CNN backbones and open datasets, including CIFAR-10/100, STL-10, LFW, and ImageNette, and further probe small training set restrictions on MNIST and Fashion-MNIST. In these settings, the proposed pooling consistently improves accuracy and weighted F1 over conventional baselines, with particularly strong gains when training data are scarce. Even with less than 1% of the training set, our method maintains reliable performance, indicating improved sample efficiency and robustness to noisy or ambiguous local patterns. Overall, integrating soft memberships into the pooling operator provides a practical and generalizable inductive bias that enhances robustness and generalization in modern CNN pipelines.}
}