  Meta-Learning (ML) has proven to be a useful tool for training Few-Shot
Learning (FSL) algorithms by exposure to batches of tasks sampled from a
meta-dataset. However, the standard training procedure overlooks the dynamic
nature of the real-world where object classes are likely to occur at different
frequencies. While it is generally understood that imbalanced tasks harm the
performance of supervised methods, there is no significant research examining
the impact of imbalanced meta-datasets on the FSL evaluation task. This study
exposes the magnitude and extent of this problem. Our results show that ML
methods are more robust against meta-dataset imbalance than imbalance at the
task-level with a similar imbalance ratio ($\rho<20$), with the effect holding
even in long-tail datasets under a larger imbalance ($\rho=65$). Overall, these
results highlight an implicit strength of ML algorithms, capable of learning
generalizable features under dataset imbalance and domain-shift. The code to
reproduce the experiments is released under an open-source license.
