Shot Transfer

Shot transfer, particularly few-shot transfer learning, focuses on adapting pre-trained models to new tasks with limited labeled data. Current research emphasizes improving efficiency and accuracy through techniques like parameter-efficient fine-tuning (e.g., using LoRA modules), carefully selecting informative features from pre-trained models, and employing novel architectures such as specialized transformers. This area is significant because it addresses the limitations of data-hungry deep learning models, enabling wider application in resource-constrained scenarios and accelerating the development of adaptable AI systems across diverse domains.