KSC 2019
Current Result Document :
| 한글제목(Korean Title) |
하이퍼파라미터 최적화를 통한 딥 컨볼루션 신경망의 성능 향상 |
| 영문제목(English Title) |
Performance Improvements of Deep Residual Convolutional Network with Hyperparameter Optimizations |
| 저자(Author) |
아바스 자파르
이명호
Abbas Jafar
Lee Myungho
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| 원문수록처(Citation) |
VOL 46 NO. 02 PP. 0013 ~ 0015 (2019. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Convolutional Neural Networks (CNNs) is one of the most commonly used deep learning models to train a large number of datasets and getting valuable results in image recognition. Deep Residual Learning (ResNet) is one of the most famous CNN for the computer vision tasks that won the ILSVR-2015 classification competition. ResNet is also one of the deepest models to train the neural networks with the idea of identity mapping for short connections. In this paper, the classification error rate of the ResNet model for the CIFAR-10 dataset is improved by optimizing hyperparameters. Our method improves performance by considering the computational complexity of the model.
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| 키워드(Keyword) |
Image recognition
Convolutional Neural Network (CNN)
Deep Residual Learning
Hyperparameters
Stochastic Gradient Descent
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| 파일첨부 |
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