Transfer learning using Tsallis entropy: An application to Gravity Spy

Abstract

Recently, transfer learning is applied as an efficient and fast method for object detection and image classification. In this paper, we propose a novel structure for transfer learning based on Tsallis entropy to reduce the loss while classifying images. Also, a comparative analysis is conducted with the traditional cross entropy in transfer learning. The results on different datasets show that transfer learning using Tsallis entropy function has higher accuracy and less loss than the classical method. Finally, the application to Gravity Spy verifies efficiency of the proposed method.