Few-Shot Learning

Few-Shot Learning is a meta-learning approach that trains a model on multiple related tasks during the meta-training phase, enabling it to generalize to unseen but related tasks with only a few samples during the meta-testing phase. The method aims to learn a general representation and then train task-specific classifiers based on this representation, thereby enhancing the model's adaptability and efficiency on new tasks.