摘要
Generally,the performance of deep learning models is related to the captured features of training samples.When the training samples belong to different domains,the diverse features may increase the difficulty of training high performance models.In this paper,we built a new framework that generates multiple models on the organized samples to increase the accuracy of classification.Firstly,our framework selects some existing models and trains each of them on organized training sets to get multiple trained models.Secondly,we select some of them based on a validation set.Finally,we use some fusion method on the outputs of the selected models to get more accurate results.The experimental results show that our framework achieved higher accuracy than the existing methods.Our framework can be an option for the deep learning system to increase the classification accuracy.
基金
National Natural Science Foundation of China(Grant Nos.61702281,61802279,6180021345 and 61702366)
Natural Science Foundation of Tianjin(Grant Nos.18JCQNJC70300,19PTZWHZ00020,19JCTPJC49200 and 19JCYBJC15800)
Fundamental Research Funds for the Tianjin Universities(Grant No.2019KJ019)
the Tianjin Science and Technology Program(Grant No.19PTZWHZ00020)
in part by the State Key Laboratory of ASIC and System(Grant No.2021KF014)
Tianjin Educational Commission Scientific Research Program Project(Grant Nos.2018KJ215 and 2020KJ112).