Performance comparison of fine-tuning and training from scratch, with three upstream datasets, six models, and four downstream datasets.

In each chart, six symbols depict the average f1-scores for the respective models, and the bar shows the mean average score across these six models.

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Bibliographic Details
Main Author:	Cuong V. Nguyen (1970134) (author)
Other Authors:	Cuong D. Do (3818461) (author)
Published:	2025
Subjects:	Biological Sciences not elsewhere classified Information Systems not elsewhere classified researchers may opt recurrent neural networks negligible cost associated lower computing cost leverage features learned downstream dataset grows convolutional neural networks necessarily improve performance achieve comparable performance series ecg applications label ecg classification deep neural networks faster training process small downstream datasets sufficiently large dataset effective ?</ p longer training time ecg datasets ecg diagnosis deep learning tuning performance larger datasets labeled datasets xlink "> world scenarios transfer learning systematically validated results underscore prevalent architectures prevailing assumption preferable choice often hindered investigating comparing available data albeit requiring accelerate convergence
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