Radial basis function neural networks (RBFs) are prime candidates for pattern classification and regression and have been used extensively in classical machine learning applications. However, RBFs have not been integrated into contemporary deep learning research and computer vision using conventional convolutional neural networks (CNNs) due to their lack of adaptability with modern architectures. In this paper, we adapt RBF networks as a classifier on top of CNNs by modifying the training process and introducing a new activation function to train modern vision architectures end-to-end for image classification. The specific architecture of RBFs enables the learning of a similarity distance metric to compare and find similar and dissimilar images. Furthermore, we demonstrate that using an RBF classifier on top of any CNN architecture provides new human-interpretable insights about the decision-making process of the models. Finally, we successfully apply RBFs to a range of CNN architectures and evaluate the results on benchmark computer vision datasets.

翻译：辐射基功能神经网络(RBF)是模式分类和回归的首选对象,并被广泛用于古典机器学习应用;然而,由于传统神经网络(CNN)缺乏适应现代结构的适应能力,RBF没有被纳入当代深层研究和计算机视野;在本论文中,我们调整RBF网络,使其在CNN之上进行分类,修改培训过程,引入新的启动功能,以培训现代视觉结构的终端至终端图像分类;RBF的具体结构使得能够学习类似距离的测量标准,以比较和查找类似和不同图像;此外,我们证明在任何CNN架构上使用RBF分类器,为模型的决策进程提供了新的人际洞察力。最后,我们成功地将RBFs应用于CNN的一系列结构,并评估计算机视觉数据集的基准结果。