论文标题
频繁网格：图像分类的新型可解释的深度学习模型
FrequentNet: A Novel Interpretable Deep Learning Model for Image Classification
论文作者
论文摘要
本文提出了一个新的基线深度学习模型，为图像分类带来了更多好处。不同于卷积神经网络（CNN）实践，该实践通过背部传播训练过滤器以表示图像的不同模式，我们的灵感来自“ PCANET：PCANET：PCANET”的方法：一个简单的深度学习基线，用于图像分类？”从频率域中的基础向量中选择滤波器向量，例如傅立叶系数或小波，而无需背部传播。研究人员表明，频域中的基础通常可以提供物理见解，这通过分析所选频率来增加模型的解释性。此外，与CNN的“黑盒”培训过程相比，培训过程还将更加效率，数学清晰和可解释。
This paper has proposed a new baseline deep learning model of more benefits for image classification. Different from the convolutional neural network(CNN) practice where filters are trained by back propagation to represent different patterns of an image, we are inspired by a method called "PCANet" in "PCANet: A Simple Deep Learning Baseline for Image Classification?" to choose filter vectors from basis vectors in frequency domain like Fourier coefficients or wavelets without back propagation. Researchers have demonstrated that those basis in frequency domain can usually provide physical insights, which adds to the interpretability of the model by analyzing the frequencies selected. Besides, the training process will also be more time efficient, mathematically clear and interpretable compared with the "black-box" training process of CNN.
