Ensembles of feedforward-designed convolutional neural networks
Abstract
An ensemble method that fuses the output decision vectors of multiple feedforward-designed convolutional neural networks (FF-CNNs) to solve the image classification problem is proposed in this work. To enhance the performance of the ensemble system, it is critical to increasing the diversity of FF-CNN models. To achieve this objective, we introduce diversities by adopting three strategies: 1) different parameter settings in convolutional layers, 2) flexible feature subsets fed into the Fully-connected (FC) layers, and 3) multiple image embeddings of the same input source. Furthermore, we partition input samples into easy and hard ones based on their decision confidence scores. As a result, we can develop a new ensemble system tailored to hard samples to further boost classification accuracy. Experiments are conducted on the MNIST and CIFAR-10 datasets to demonstrate the effectiveness of the ensemble method.
Cite
@article{arxiv.1901.02154,
title = {Ensembles of feedforward-designed convolutional neural networks},
author = {Yueru Chen and Yijing Yang and Wei Wang and C. -C. Jay Kuo},
journal= {arXiv preprint arXiv:1901.02154},
year = {2019}
}