RETRACTED ARTICLE: Improved transfer learning of CNN through fine-tuning and classifier ensemble for scene classification

Thirumaladevi, S.; Swamy, K. Veera; Sailaja, M.

doi:10.1007/s00500-022-07145-1

Cited by 5 publications

(1 citation statement)

References 27 publications

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“…Conceptually the base model layer is part of feature learning and classification [84]. The main challenge in determining the base model layer that fits the background of the research case has to have different experimental approaches, as was done by [24], [25], [29], [35], [36] to produce the best model, when the transfer learning process is carried out from the pre-trained model to the base model layer.…”

Section: Pre-trained Modelmentioning

confidence: 99%

CNN Ensemble Learning Method for Transfer learning: A Review

Sulistya,

Br Bangun,

Tyas

2023

Ilk. J. Ilm.

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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, f1score, 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,

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