Transfer Learning: Bridging Domains in Machine Learning - Devi, Gayathri (AG Volumes, 2024)
Transfer learning uses pre-trained information from a large-scale dataset (like ImageNet) to a target task with sparsely labeled data. This strategy improves learning performance and generalization by enabling models to draw on the learnt representations and efficiently apply information to new challenges. Transfer learning has been used extensively in many fields, and its use is growing as computer networks, information technology, and related businesses grow quickly. Transfer learning offers excellent growth chances and tremendous development potential. The TL approach has a number of benefits, especially in domains that use images. These benefits include minimizing the scarcity of data, avoiding overfitting, quickening the pace of convergence, enhancing the quality of extracted features and data reuse, facilitating simulation training, cutting down on training time, and enhancing generalization ability.