Adaptive convolution kernel for artificial neural networks?


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Tek F. B. , Cam I., Karli D.

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, vol.75, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 75
  • Publication Date: 2021
  • Doi Number: 10.1016/j.jvcir.2020.103015
  • Journal Name: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Communication & Mass Media Index, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC
  • Keywords: Adaptive convolution, Multi-scale convolution, Image classification, Residual networks
  • Istanbul Technical University Affiliated: No

Abstract

Many deep neural networks are built by using stacked convolutional layers of fixed and single size (often 3 ? 3) kernels. This paper describes a method for learning 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 ordinary convolution layers in a U-shaped network with 7 ? 7 adaptive layers can improve its learning performance and ability to generalize.