# PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks Source: [PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:35:00.393Z Authors: Xueli Xiao, Chunyan Ji, Thosini Bamunu Mudiyanselage, Yi Pan Published: 2020-09-24T18:31:35.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()