  Few-Shot Learning (FSL) algorithms are commonly trained through Meta-Learning
(ML), which exposes models to batches of tasks sampled from a meta-dataset to
mimic tasks seen during evaluation. However, the standard training procedures
overlook the real-world dynamics where classes commonly occur at different
frequencies. While it is generally understood that class imbalance harms the
performance of supervised methods, limited research examines the impact of
imbalance on the FSL evaluation task. Our analysis compares 10 state-of-the-art
meta-learning and FSL methods on different imbalance distributions and
rebalancing techniques. Our results reveal that 1) some FSL methods display a
natural disposition against imbalance while most other approaches produce a
performance drop by up to 17\% compared to the balanced task without the
appropriate mitigation; 2) contrary to popular belief, many meta-learning
algorithms will not automatically learn to balance from exposure to imbalanced
training tasks; 3) classical rebalancing strategies, such as random
oversampling, can still be very effective, leading to state-of-the-art
performances and should not be overlooked; 4) FSL 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 ($\rho=65$).
