  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.
