Shot Tuning

Shot tuning, a technique for efficiently adapting large pre-trained models to new tasks with limited data, aims to reduce the computational cost and data requirements of fine-tuning. Current research focuses on improving the robustness and performance of shot tuning across various model architectures, including diffusion models, vision transformers, and large language models, often employing techniques like Bayesian neural networks, contrastive learning, and parameter-efficient methods such as LoRA. These advancements are significant because they enable the deployment of powerful models in resource-constrained settings and facilitate personalized AI applications across diverse domains.