Adaptive Convolution Kernel for Artificial Neural Networks

• URL: http://arxiv.org/abs/2009.06385v1
• Date: Mon, 14 Sep 2020 12:36:50 GMT
• Title: Adaptive Convolution Kernel for Artificial Neural Networks
• Authors: F. Boray Tek, \.Ilker \c{C}am, Deniz Karl{\i}
• Abstract summary: This paper describes a method for training the size of convolutional kernels to provide varying size kernels in a single layer. Experiments compared the proposed adaptive layers to ordinary convolution layers in a simple two-layer network. A segmentation experiment in the Oxford-Pets dataset demonstrated that replacing a single ordinary convolution layer in a U-shaped network with a single 7$times$7 adaptive layer can improve its learning performance and ability to generalize.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3$\times$3) kernels. This paper describes a method for training the size of convolutional kernels to provide varying size kernels in a single layer. The method utilizes a differentiable, and therefore backpropagation-trainable Gaussian envelope which can grow or shrink in a base grid. Our experiments compared the proposed adaptive layers to ordinary convolution layers in a simple two-layer network, a deeper residual network, and a U-Net architecture. The results in the popular image classification datasets such as MNIST, MNIST-CLUTTERED, CIFAR-10, Fashion, and ``Faces in the Wild'' showed that the adaptive kernels can provide statistically significant improvements on ordinary convolution kernels. A segmentation experiment in the Oxford-Pets dataset demonstrated that replacing a single ordinary convolution layer in a U-shaped network with a single 7$\times$7 adaptive layer can improve its learning performance and ability to generalize.

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