Efficient Domain Adaptation
Efficient domain adaptation focuses on adapting pre-trained models to new, data-scarce domains without extensive retraining, aiming to improve performance while minimizing computational cost and preserving model efficiency. Current research emphasizes parameter-efficient techniques, such as using adapters (small, trainable modules added to existing models) or leveraging generative priors and optimization-based methods to adjust model behavior for the target domain. This research area is significant because it enables the application of powerful, large models to diverse tasks with limited data, improving the practicality and scalability of machine learning across various fields, including speech recognition, image processing, and natural language processing.