Kernel function impact on convolutional neural networks

• URL: http://arxiv.org/abs/2302.10266v1
• Date: Mon, 20 Feb 2023 19:57:01 GMT
• Title: Kernel function impact on convolutional neural networks
• Authors: M.Amine Mahmoudi, Aladine Chetouani, Fatma Boufera, Hedi Tabia
• Abstract summary: We study the usage of kernel functions at the different layers in a convolutional neural network. We show how one can effectively leverage kernel functions, by introducing a more distortion aware pooling layers. We propose Kernelized Dense Layers (KDL), which replace fully-connected layers.
• Score: 10.98068123467568
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the usage of kernel functions at the different layers in a convolutional neural network. We carry out extensive studies of their impact on convolutional, pooling and fully-connected layers. We notice that the linear kernel may not be sufficiently effective to fit the input data distributions, whereas high order kernels prone to over-fitting. This leads to conclude that a trade-off between complexity and performance should be reached. We show how one can effectively leverage kernel functions, by introducing a more distortion aware pooling layers which reduces over-fitting while keeping track of the majority of the information fed into subsequent layers. We further propose Kernelized Dense Layers (KDL), which replace fully-connected layers, and capture higher order feature interactions. The experiments on conventional classification datasets i.e. MNIST, FASHION-MNIST and CIFAR-10, show that the proposed techniques improve the performance of the network compared to classical convolution, pooling and fully connected layers. Moreover, experiments on fine-grained classification i.e. facial expression databases, namely RAF-DB, FER2013 and ExpW demonstrate that the discriminative power of the network is boosted, since the proposed techniques improve the awareness to slight visual details and allows the network reaching state-of-the-art results.

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