Transfer learning has revolutionized model training by allowing practitioners to leverage pre-trained models for new, related tasks, significantly reducing the need for extensive labeled data and computational resources. This method is particularly beneficial in fields like computer vision and natural language processing, where models can be fine-tuned to perform specific tasks with limited training data, thus enhancing efficiency and performance [1][3].

By utilizing models initially trained on large datasets, like ImageNet, transfer learning accelerates training processes and often leads to better results compared to training models from scratch. This has made deep learning more accessible, enabling applications in diverse areas, including medical diagnostics and financial forecasting [4].

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