In the context of radio galaxy classification, most state-of-the-art neural
network algorithms have been focused on single survey data. The question of
whether these trained algorithms have cross-survey identification ability or
can be adapted to develop classification networks for future surveys is still
unclear. One possible solution to address this issue is transfer learning,
which re-uses elements of existing machine learning models for different
applications. Here we present radio galaxy classification based on a 13-layer
Deep Convolutional Neural Network (DCNN) using transfer learning methods
between different radio surveys. We find that our machine learning models
trained from a random initialization achieve accuracies comparable to those
found elsewhere in the literature. When using transfer learning methods, we
find that inheriting model weights pre-trained on FIRST images can boost model
performance when re-training on lower resolution NVSS data, but that inheriting
pre-trained model weights from NVSS and re-training on FIRST data impairs the
performance of the classifier. We consider the implication of these results in
the context of future radio surveys planned for next-generation radio
telescopes such as ASKAP, MeerKAT, and SKA1-MID