# What's mutual exclusivity in meta-learning?

What do we mean by mutual exclusivity of tasks?

This work (E Pan, 21) and this one (M Yin, 20) state that most classification meta-learning algorithms fail for non-mutually exclusive tasks as the model may over-fit to a task, and no model can solve all the tasks at once (respectively).

I had trouble understanding the exact meaning of a "task" in meta classification here. [E Pan, 21] uses "task" synonymously with "new class", while [M Yin, 20] states "...prior work uses a per-task random assignment of image classes to N-way classification labels". However, some priors on few-shot learning [S. Hugo, 17], and [Y Wang, 19] agree with FFLab's, (20) description of "task" which I found more clear:

The number of classes (N) in the support set defines a task as an N-class classification task or N-way task, and the number of labeled examples in each class (k) corresponds to k-shot, making it an N-way, k-shot learning problem.

Where the support set $$D_s$$ here is part of the meta training data $$D$$ which comprises a support and test set $$D_t$$ $$D = $$ [Weng, 18].

However, even with a better understanding of what a "task" is, I still couldn't get what constitutes mutually exclusive tasks.