Fine-Tuning Strategies for Transfer Learning Models to Address Domain-Specific Challenges in Low-Resource Settings
Keywords:
Transfer Learning, Fine-Tuning Strategies, Domain Adaptation, Low-Resource Settings, Data Augmentation, Adaptive Learning RatesAbstract
Transfer learning has emerged as a pivotal technique in machine learning, particularly for low-resource settings where annotated data is sparse. This paper explores fine-tuning strategies tailored for domain-specific challenges in such settings. Leveraging pre-trained models, these strategies focus on efficient domain adaptation, minimizing overfitting, and optimizing resource utilization. Key methodologies include task-specific head tuning, layer-wise freezing, data augmentation, and adaptive learning rates. Empirical evidence demonstrates significant performance gains across applications such as natural language processing and computer vision. The findings contribute to the growing body of knowledge on deploying robust AI systems in low-resource domains, fostering practical solutions in healthcare, education, and beyond.
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