Fatma Harby1, Adel Thaljaoui1, Durre Nayab2, Suliman Aladhadh3,*, Salim EL Khediri3,4, Rehan Ullah Khan3
CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5191-5207, 2023, DOI:10.32604/cmc.2022.029420
- 28 December 2022
Abstract In the machine learning (ML) paradigm, data augmentation serves
as a regularization approach for creating ML models. The increase in the
diversification of training samples increases the generalization capabilities,
which enhances the prediction performance of classifiers when tested on
unseen examples. Deep learning (DL) models have a lot of parameters, and
they frequently overfit. Effectively, to avoid overfitting, data plays a major
role to augment the latest improvements in DL. Nevertheless, reliable data
collection is a major limiting factor. Frequently, this problem is undertaken
by combining augmentation of data, transfer learning, dropout, and methods
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