عنوان انگلیسی مقاله:
iFusion: Towards efficient intelligence fusion for deep learning from real-time and heterogeneous data
ترجمه فارسی عنوان مقاله:
iFusion: به سمت تلفیق اطلاعاتی کارآمد برای یادگیری عمیق از داده های واقعی و ناهمگن
Sciencedirect - Elsevier - Information Fusion, 51 (2019) 215-223: doi:10:1016/j:inffus:2019:02:008
Kehua Guo a , Tao Xu a , Xiaoyan Kui a , ∗ , Ruifang Zhang a , Tao Chi b
Deep learning has shown great strength in many fields and has allowed people to live more conveniently and intelligently. However, deep learning requires a considerable amount of uniform training data, which introduces difficulties in many application scenarios. On the one hand, in real-time systems, training data are constantly generated, but users cannot immediately obtain this vast amount of training data. On the other hand, training data from heterogeneous sources have different data formats. Therefore, existing deep learning frameworks are not able to train all data together. In this paper, we propose the iFusion framework, which achieves efficient intelligence fusion for deep learning from real-time data and heterogeneous data. For real-time data, we train only newly arrived data to obtain a new discrimination model and fuse the previously trained models to obtain the discrimination result. For heterogeneous data, different types of data are trained separately; then, we fuse the different discrimination models so that it is not necessary to consider heterogeneous data formats. We use a method based on Dempster-Shafer theory (DST) to fuse the discrimination models. We apply iFusion to the deep learning of medical image data, and the results of the experiments show the effectiveness of the proposed method.
Keywords: Information| fusion | Real-time data | Heterogeneous data | Deep learning