Shot Fine Tuning

Few-shot fine-tuning aims to adapt pre-trained models to new tasks using minimal labeled data, significantly reducing training costs and enabling personalized AI applications. Current research focuses on mitigating issues like the emergence of noisy patterns during training (especially in diffusion models), improving generalization by rephrasing inputs or incorporating self-training mechanisms, and comparing its performance against in-context learning across various model architectures, including language models (e.g., BERT, BART) and diffusion models. This approach holds significant promise for advancing various fields, from computer vision (object detection in remote sensing) and natural language processing (text classification, named entity recognition) to improving the robustness and efficiency of AI systems.