Solving the Feature Diversity Problem Based on Multi-Model Scheme
Guanghao Jin1, Na Zhao1, Chunmei Pei1, Hengguang Li2, Qingzeng Song3, Jing Yu1,*
Journal on Artificial Intelligence, Vol.3, No.4, pp. 135-143, 2021, DOI:10.32604/jai.2021.027154
- 07 February 2022
Abstract 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 More >