Domain Few Shot

Domain few-shot learning aims to train machine learning models that can accurately classify new data from unseen domains using only a limited number of examples. Current research focuses on adapting large language models and other foundation models for this purpose, exploring techniques like data augmentation, prototype learning, and adversarial training to improve performance and address the challenges of domain shift. This field is significant because it enables the development of more robust and adaptable AI systems, reducing the reliance on massive datasets and facilitating applications in diverse areas such as object counting, event extraction, and anomaly detection.