Shot Keyword

Few-shot learning aims to train effective models with minimal training data, addressing the limitations of traditional machine learning approaches that require vast datasets. Current research focuses on improving few-shot performance across various tasks, including keyword spotting, named entity recognition, and scene graph generation, employing techniques like linear probes, multi-task learning with auxiliary data, and meta-learning for improved parameter efficiency and performance prediction. These advancements are significant because they enable the development of more adaptable and robust AI systems capable of handling real-world scenarios with limited labeled data, impacting fields such as speech recognition, natural language processing, and robotics.