  We propose a novel approach for visual representation learning called
Signature-Graph Neural Networks (SGN). SGN learns latent global structures that
augment the feature representation of Convolutional Neural Networks (CNN). SGN
constructs unique undirected graphs for each image based on the CNN feature
maps. The feature maps are partitioned into a set of equal and non-overlapping
patches. The graph nodes are located on high-contrast sharp convolution
features with the local maxima or minima in these patches. The node embeddings
are aggregated through novel Signature-Graphs based on horizontal and vertical
edge connections. The representation vectors are then computed based on the
spectral Laplacian eigenvalues of the graphs. SGN outperforms existing methods
of recent graph convolutional networks, generative adversarial networks, and
auto-encoders with image classification accuracy of 99.65% on ASIRRA, 99.91% on
MNIST, 98.55% on Fashion-MNIST, 96.18% on CIFAR-10, 84.71% on CIFAR-100, 94.36%
on STL10, and 95.86% on SVHN datasets. We also introduce a novel implementation
of the state-of-the-art multi-head attention (MHA) on top of the proposed SGN.
Adding SGN to MHA improved the image classification accuracy from 86.92% to
94.36% on the STL10 dataset
