CNN Ensemble learning method for Transfer learning: A Review
This study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Fakultas Ilmu Komputer UMI
2023-04-01
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| Series: | Ilkom Jurnal Ilmiah |
| Subjects: | |
| Online Access: | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1541 |
| _version_ | 1797850445736050688 |
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| author | Yudha Islami Sulistya Elsi Titasari Br Bangun Dyah Aruming Tyas |
| author_facet | Yudha Islami Sulistya Elsi Titasari Br Bangun Dyah Aruming Tyas |
| author_sort | Yudha Islami Sulistya |
| collection | DOAJ |
| description | This study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning ensemble, and transfer learning is growing every year. In 2022, there will be 35 papers reviewed related to this topic in this study. Some datasets contain apparent information starting from the dataset name, total data points, dataset splitting, target dataset availability, and type classification. ResNet-50, VGG-16, InceptionV3, and VGG-19 are used in most papers as pre-trained models and transfer learning processes. 50 (90.1%) papers use ensemble learning, and 5 (9.1%) do without ensemble learning. The reviewed paper summarizes several performance measurements, including accuracy, precision, recall, f1-score, sensitivity, specificity, training accuracy, validation accuracy, test accuracy, training losses, validation losses, test losses, training time, and AUC, DSC. In the last section, 49 papers produce the best model performance using the proposed model, and 6 other papers use DenseNet, DeQueezeNet, Extended Yager Model, InceptionV3, and ResNet-152. |
| first_indexed | 2024-04-09T19:00:22Z |
| format | Article |
| id | doaj.art-8fa75ac5619f4e53b0055eb96bf3f022 |
| institution | Directory Open Access Journal |
| issn | 2087-1716 2548-7779 |
| language | English |
| last_indexed | 2024-04-09T19:00:22Z |
| publishDate | 2023-04-01 |
| publisher | Fakultas Ilmu Komputer UMI |
| record_format | Article |
| series | Ilkom Jurnal Ilmiah |
| spelling | doaj.art-8fa75ac5619f4e53b0055eb96bf3f0222023-04-08T08:20:44ZengFakultas Ilmu Komputer UMIIlkom Jurnal Ilmiah2087-17162548-77792023-04-01151456310.33096/ilkom.v15i1.1541.45-63491CNN Ensemble learning method for Transfer learning: A ReviewYudha Islami Sulistya0Elsi Titasari Br Bangun1Dyah Aruming Tyas2Universitas Gadjah MadaUniversitas Gadjah MadaUniversitas Gadjah MadaThis study provides a review of CNN's ensemble learning method for transfer learning by highlighting sections such as review studies, datasets, pre-trained models, transfer learning, ensemble learning, and performance. The results indicate that the trend of ensemble learning, transfer learning ensemble, and transfer learning is growing every year. In 2022, there will be 35 papers reviewed related to this topic in this study. Some datasets contain apparent information starting from the dataset name, total data points, dataset splitting, target dataset availability, and type classification. ResNet-50, VGG-16, InceptionV3, and VGG-19 are used in most papers as pre-trained models and transfer learning processes. 50 (90.1%) papers use ensemble learning, and 5 (9.1%) do without ensemble learning. The reviewed paper summarizes several performance measurements, including accuracy, precision, recall, f1-score, sensitivity, specificity, training accuracy, validation accuracy, test accuracy, training losses, validation losses, test losses, training time, and AUC, DSC. In the last section, 49 papers produce the best model performance using the proposed model, and 6 other papers use DenseNet, DeQueezeNet, Extended Yager Model, InceptionV3, and ResNet-152.https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1541ensemble learningtransfer learningdeep learningpre-trained modelcnn |
| spellingShingle | Yudha Islami Sulistya Elsi Titasari Br Bangun Dyah Aruming Tyas CNN Ensemble learning method for Transfer learning: A Review Ilkom Jurnal Ilmiah ensemble learning transfer learning deep learning pre-trained model cnn |
| title | CNN Ensemble learning method for Transfer learning: A Review |
| title_full | CNN Ensemble learning method for Transfer learning: A Review |
| title_fullStr | CNN Ensemble learning method for Transfer learning: A Review |
| title_full_unstemmed | CNN Ensemble learning method for Transfer learning: A Review |
| title_short | CNN Ensemble learning method for Transfer learning: A Review |
| title_sort | cnn ensemble learning method for transfer learning a review |
| topic | ensemble learning transfer learning deep learning pre-trained model cnn |
| url | https://jurnal.fikom.umi.ac.id/index.php/ILKOM/article/view/1541 |
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