Shot Task

Few-shot learning (FSL) aims to train models that can effectively learn new tasks from only a handful of examples, mimicking human learning capabilities. Current research focuses on improving FSL performance across various modalities (image, audio, text) using techniques like meta-learning, prototype-based methods, and transfer learning from large pre-trained models, often addressing challenges like task contamination and feature redundancy. These advancements are significant because they enable efficient model adaptation to new data, reducing the need for extensive labeled datasets and paving the way for more robust and adaptable AI systems in diverse applications.