An Empirical Investigation of Pre-Trained Deep Learning Model Reuse in the Scientific Process

This study presents an empirical analysis of pre-trained deep learning model (PTM) reuse in the natural sciences, examining 17,718 peer-reviewed papers. It reveals that the field of "Biochemistry, Genetics and Molecular Biology" leads in PTM utilization, with "adaptation" identified as the most common reuse pattern, significantly influencing the "testing" stage of the scientific process. This research underscores the importance of PTMs in reducing the costs and barriers associated with deep learning model training, providing insights for practitioners on effective reuse strategies.