앙상블 학습 알고리즘을 이용한 컨벌루션 신경망의 분류 성능 분석에 관한 연구

• Published in: 멀티미디어학회논문지 Vol. 22; no. 6; pp. 665 - 675
• Main Authors: 박성욱(Sung-Wook Park), 김종찬(Jong-Chan Kim), 김도연(Do-Yeon Kim)
• Format: Journal Article
• Language: Korean
• Published: 한국멀티미디어학회 2019
• Subjects: 전자/정보통신공학; CNN; Computer Vision; Deep Learning; Ensemble Learning Algorithm
• ISSN: 1229-7771; 2384-0102
• DOI: 10.9717/kmms.2019.22.6.665

In this paper, we compare and analyze the classification performance of deep learning algorithm Convolutional Neural Network(CNN) ac cording to ensemble generation and combining techniques. We used several CNN models(VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, GoogLeNet) to create 10 ensemble generation combinations and applied 6 combine techniques(average, weighted average, maximum, minimum, median, product) to the optimal combination. Experimental results, DenseNet169-VGG16-GoogLeNet combination in ensemble generation, and the product rule in ensemble combination showed the best performance. Based on this, it was concluded that ensemble in different models of high benchmarking scores is another way to get good results.