한국멀티미디어학회논문지 (Journal of Korea Multimedia Society)

  • 제21권12호
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  • Pages.1387-1395
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  • 2018
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  • 1229-7771(pISSN)
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  • 2384-0102(eISSN)
한국멀티미디어학회 (Korea Multimedia Society)
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전이학습에 방법에 따른 컨벌루션 신경망의 영상 분류 성능 비교

Comparison of Image Classification Performance in Convolutional Neural Network according to Transfer Learning

  • 투고 : 2018.08.28
  • 심사 : 2018.11.07
  • 발행 : 2018.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

Core algorithm of deep learning Convolutional Neural Network(CNN) shows better performance than other machine learning algorithms. However, if there is not sufficient data, CNN can not achieve satisfactory performance even if the classifier is excellent. In this situation, it has been proven that the use of transfer learning can have a great effect. In this paper, we apply two transition learning methods(freezing, retraining) to three CNN models(ResNet-50, Inception-V3, DenseNet-121) and compare and analyze how the classification performance of CNN changes according to the methods. As a result of statistical significance test using various evaluation indicators, ResNet-50, Inception-V3, and DenseNet-121 differed by 1.18 times, 1.09 times, and 1.17 times, respectively. Based on this, we concluded that the retraining method may be more effective than the freezing method in case of transition learning in image classification problem.

키워드

MTMDCW_2018_v21n12_1387_f0001.png 이미지

Fig. 1. Weight freezing method of transfer learning.

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Fig. 2. Weight retraining method of transfer learning.

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Fig. 3. Example of a cropped insect image.

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Fig. 4. Representative insect images used in the experiment.

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Fig. 5. Comparison of accuracy and loss rates up to 100 epoch without early stopping in ResNet-50

MTMDCW_2018_v21n12_1387_f0006.png 이미지

Fig. 6. Comparison of Accuracy and loss rates up to 100 epoch without early stopping in Inception-V3.

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Fig. 7. Comparison of Accuracy and loss rates up to 100 epoch without early stopping in DenseNet-121.

Table 1. Data set information

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Table 2. Operating system and middleware information used in the experiment

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Table 3. Comparison of the two transfer learning results in ResNet-50

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Table 4. The precision, recall, and f-score of ResNet-50

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Table 5. Comparison of the two transfer learning results in Inception-V3

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Table 6. The precision, recall, and f-score of Inception-V3

MTMDCW_2018_v21n12_1387_t0006.png 이미지

Table 7. Comparison of the two transfer learning results in DenseNet-121

MTMDCW_2018_v21n12_1387_t0007.png 이미지

Table 8. The precision, recall, and f-score of DenseNet-121

MTMDCW_2018_v21n12_1387_t0008.png 이미지

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