Before revision:  The results show that ConTraKG is significantly more accurate than the conventional approach for dealing with domain changes. In a transfer learning setup, where the network is trained on both domains, ConTraKG achieves 21\% higher accuracy when tested on the source domain and 15\% when tested on the target domain compared to the standard approach. Moreover, with only 10\% of the target data for training, it achieves the same accuracy as the cross-entropy-based model trained on the full target data .
After revision:  The results show that a visual model trained with a knowledge graph as a trainer outperforms a model trained with cross-entropy in all experiments, in particular when the domain gap increases. Besides better performance and stronger robustness to domain shifts, these KG-NN adapts to multiple datasets and classes without suffering heavily from catastrophic URLetting .