ABSTRACT
In the general case, the performance of deep learning-based classification models depends on the ability of capturing features. When a sample has various appearances, the increased features may lower the performance of these models. In this case, training more models on different appearances can be a choice to improve the accuracy. In this paper, we built a new framework that generates a network of models to improve the accuracy. First, our framework built a strategy to increase the number of models to well capture the increased features. We then utilise our recursive Bayesian methods on the selected outputs of trained models, which is to reduce the similarity among these outputs for higher accuracy. The experimental results show that our framework can be a good choice to improve the performance of deep learning applications.
Disclosure statement
No potential conflict of interest was reported by the author(s).