One Shot Learning

One-Shot Learning refers to the task of learning object category information from a single training sample. Its goal is to achieve efficient learning and recognition with extremely limited data, significantly reducing data requirements and enhancing the model's generalization ability. This method has significant application value in scenarios such as biometric recognition and image classification.