论文标题
卷积神经网络的径向基函数网络学习相似性距离并提高可解释性
Radial Basis Function Networks for Convolutional Neural Networks to Learn Similarity Distance Metric and Improve Interpretability
论文作者
论文摘要
径向基函数神经网络（RBF）是用于模式分类和回归的主要候选者，并且已在经典的机器学习应用中广泛使用。但是，由于缺乏现代体系结构的适应性，RBF尚未使用常规卷积神经网络（CNN）纳入当代深度学习研究和计算机视觉。在本文中，我们通过修改训练过程并引入新的激活功能来将RBF网络作为分类器在CNN之上，以训练现代视觉体系结构端到端以进行图像分类。 RBF的特定架构使学习相似性距离度量可以比较并找到相似和不同的图像。此外，我们证明，在任何CNN体系结构上使用RBF分类器都提供了有关模型决策过程的新的人类解剖见解。最后，我们成功地将RBF应用于一系列CNN架构，并在基准计算机视觉数据集上评估结果。
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.
