Transfer Learning on Trial: A Case Study to Apply Existing Models to Heterogeneous Datasets

Abstract

Nowadays, transfer learning is getting more and more popular in both industry and academia. It enables people to benefit from current advanced AI technologies, which used to be only accessible to professional teams with the most powerful talents, software and hardware resources. It has been proved that transfer learning is the best available option to apply learned patterns for one problem to a different but related problem. But rare research has been done to evaluate the performance of employing an existing model to a less related problem. In this paper, we apply the pre-trained model in the computer vision field, VGG, to a radar dataset, Ionosphere, which is heterogeneous to the above vision data, and carry out extensive experiments. The results show that the classification accuracy is much lower than that in the early research work, and the application of transfer learning should depend on certain situations.