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F-Transform and Convolutional NN: Cross-Fertilization and Step Forward

机译:F变换和卷积NN:交叉应用和向前发展

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We propose to assign the F-transform kernels to the CNN weights and compare them with commonly used initialization. By this, we develop a new initialization mechanism where the F-transform convolution kernels are used in the convolutional layers. Based on a series of experiments, we demonstrate the suitability of the F-transform-based deep neural network in the domain of image processing with the focus on classification. Moreover, we support our insight by revealing the similarity between the F-transform and first-layer kernels in certain deep neural networks.
机译:我们建议将F变换内核分配给CNN权重,并将它们与常用初始化进行比较。由此,我们开发了一种新的初始化机制,其中F变换卷积内核用于卷积层。基于一系列实验,我们展示了基于F变换的深神经网络在图像处理领域的适用性,重点是分类。此外,我们通过揭示某些深神经网络中的F变换和第一层内核之间的相似性来支持我们的洞察力。

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