Before revision:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space  according to the domain-invariant embedding space of the knowledge graph based on a contrastive loss function. This allows the neural network to incorporate training data from different target domains that are already represented in the knowledge graph. We conduct a series of empirical evaluations to determine the accuracy of our approach.
After revision:  A knowledge graph serves as a medium for encoding such prior knowledge , which is then transformed into a dense vector representation via embedding methods . Using a five-phase training pipeline, we train the deep neural network to adjust its visual embedding space according to the image-data invariant knowledge graph embedding space. We evaluate KG-NN on visual transfer learning tasks for classification using the mini-ImageNet dataset and its derivatives, as well as road sign recognition datasets from Germany and China.