Although meta-learning seems promising performance in the realm of rapid adaptability, it is constrained by fixed cardinality. When faced with tasks of varying cardinalities that were unseen during training, the model lacks its ability. In this paper, we address and resolve this challenge by harnessing `label equivalence' emerged from stochastic numeric label assignments during episodic task sampling. Questioning what defines ``true" meta-learning, we introduce the ``any-way" learning paradigm, an innovative model training approach that liberates model from fixed cardinality constraints. Surprisingly, this model not only matches but often outperforms traditional fixed-way models in terms of performance, convergence speed, and stability. This disrupts established notions about domain generalization. Furthermore, we argue that the inherent label equivalence naturally lacks semantic information. To bridge this semantic information gap arising from label equivalence, we further propose a mechanism for infusing semantic class information into the model. This would enhance the model's comprehension and functionality. Experiments conducted on renowned architectures like MAML and ProtoNet affirm the effectiveness of our method.

翻译：尽管元学习在快速适应领域展现出令人期待的性能，但其受限于固定的类别数量。当面对训练过程中未见过的不同类别数量的任务时，该模型缺乏相应能力。本文通过利用任务片段采样过程中随机数值标签分配产生的"标签等价性"，解决并攻克了这一挑战。我们质疑何为"真正"的元学习，提出了"任意类别"学习范式——一种创新的模型训练方法，使模型摆脱了固定类别数量的限制。令人惊讶的是，该模型不仅与传统固定类别模型性能相当，在收敛速度和稳定性方面往往更胜一筹。这一发现颠覆了关于领域泛化的既有认知。此外，我们认为固有的标签等价性天然缺乏语义信息。为弥补标签等价性导致的语义信息鸿沟，我们进一步提出了一种将语义类别信息注入模型的机制。这将增强模型的理解能力与功能性。在MAML和ProtoNet等知名架构上进行的实验验证了本方法的有效性。