Unsupervised Few-Shot Learning

Unsupervised few-shot learning is a method where, during the pre-training or meta-training phase, the model is trained solely on an unlabeled dataset. The goal is to enable the model to quickly adapt to new tasks with only a few labeled samples by learning the intrinsic structure and features of the data, thereby enhancing its generalization capability. This approach has significant application value in computer vision, effectively addressing the issue of scarce labeled data and improving the model's practicality and flexibility.