Low Shot

Low-shot learning aims to train effective machine learning models with minimal labeled data, addressing the limitations of traditional approaches requiring massive datasets. Current research focuses on leveraging pre-trained vision-language models, self-supervised learning techniques (like contrastive learning and masked image modeling), and innovative architectures such as masked autoencoders and Gaussian process ensembles to improve performance in low-data scenarios. These advancements are crucial for expanding the applicability of AI to domains with limited annotated data, impacting fields like object recognition, image classification, and relation extraction. The development of robust low-shot learning methods is vital for making AI more efficient and accessible.