Transfer learning for radio galaxy classification (Submitted on 28 Mar 2019)
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by zutopian
Transfer learning for radio galaxy classification
Hongming Tang, Anna M. M. Scaife, J.P. Leahy
(Submitted on 28 Mar 2019)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.(...)
https://arxiv.org/abs/1903.11921
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