Abstract
Kernel size selection in Convolutional Neural Networks (CNNs) is a critical but often overlooked design decision that affects receptive field, feature extraction, computational cost, and model accuracy. This paper proposes the Best Kernel Size Estimation Function (BKSEF), a mathematically grounded and empirically validated framework for optimal, layer-wise kernel size determination. BKSEF balances information gain, computational efficiency, and accuracy improvements by integrating principles from information theory, signal processing, and learning theory. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet-lite, ChestX-ray14, and GTSRB datasets demonstrate that BKSEF-guided architectures achieve up to 3.1 percent accuracy improvement and 42.8 percent reduction in FLOPs compared to traditional models using uniform 3x3 kernels. Two real-world case studies further validate the approach: one for medical image classification in a cloud-based setup, and another for traffic sign recognition on edge devices. The former achieved enhanced interpretability and accuracy, while the latter reduced latency and model size significantly, with minimal accuracy trade-off. These results show that kernel size can be an active, optimizable parameter rather than a fixed heuristic. BKSEF provides practical heuristics and theoretical support for researchers and developers seeking efficient and application-aware CNN designs. It is suitable for integration into neural architecture search pipelines and real-time systems, offering a new perspective on CNN optimization.
Abstract (translated)
卷积神经网络（CNN）中核大小的选择是一项关键但常常被忽视的设计决策，它影响着感受野、特征提取、计算成本和模型精度。本文提出了最佳核尺寸估算函数（BKSEF），这是一个基于数学理论并经过实证验证的框架，用于确定最优的层间核大小。BKSEF通过整合信息论、信号处理及学习理论的原则，在信息增益、计算效率以及准确性提升之间取得了平衡。 在CIFAR-10、CIFAR-100、ImageNet-lite、ChestX-ray14和GTSRB等数据集上进行的广泛实验表明，与使用统一3x3核的传统模型相比，采用BKSEF引导架构可以实现高达3.1%的准确性提升以及最多42.8%的浮点运算次数（FLOPs）减少。 两个真实世界案例进一步验证了该方法的有效性：一个是基于云环境下的医学图像分类任务，另一个是在边缘设备上进行交通标志识别。前者实现了更高的可解释性和准确率，而后者则显著降低了延迟和模型大小，并且几乎没有准确性损失。 这些结果表明核尺寸可以成为一种主动优化参数，而非固定的经验法则。BKSEF为寻求高效且应用场景敏感的CNN设计的研究人员和开发者提供了实用经验和理论支持。该方法适合集成到神经架构搜索流程中及实时系统内，提供了一种新的视角来优化CNN结构。
URL
https://arxiv.org/abs/2506.14846
