One Shot Learning

One-shot learning aims to enable machines to learn new concepts or tasks from a single example, mirroring human learning capabilities. Current research focuses on improving the efficiency and accuracy of one-shot learning across diverse applications, including instruction tuning for large language models, medical image segmentation, and robotic control, employing techniques like neural network weight imprinting and biologically-inspired architectures. These advancements are significant because they reduce the massive datasets typically required for training, leading to more efficient and potentially more robust AI systems across various fields.