  Deep learning has gained great success in various classification tasks.
Typically, deep learning models learn underlying features directly from data,
and no underlying relationship between classes are included. Similarity between
classes can influence the performance of classification. In this article, we
propose a method that incorporates class similarity knowledge into
convolutional neural networks models using a graph convolution layer. We
evaluate our method on two benchmark image datasets: MNIST and CIFAR10, and
analyze the results on different data and model sizes. Experimental results
show that our model can improve classification accuracy, especially when the
amount of available data is small.
