Few-shot learning aims to train models on a limited number of labeled samples given in a support set in order to generalize to unseen samples from a query set. In the standard setup, the support set contains an equal amount of data points for each class. However, this assumption overlooks many practical considerations arising from the dynamic nature of the real world, such as class-imbalance. In this paper, we present a detailed study of few-shot class-imbalance along three axes: meta-dataset vs. task imbalance, effect of different imbalance distributions (linear, step, random), and effect of rebalancing techniques. We extensively compare over 10 state-of-the-art few-shot learning and meta-learning methods using unbalanced tasks and meta-datasets. Our analysis using Mini-ImageNet reveals that 1) compared to the balanced task, the performances on class-imbalance tasks counterparts always drop, by up to $18.0\%$ for optimization-based methods, and up to $8.4$ for metric-based methods, 2) contrary to popular belief, meta-learning algorithms, such as MAML, do not automatically learn to balance by being exposed to imbalanced tasks during (meta-)training time, 3) strategies used to mitigate imbalance in supervised learning, such as oversampling, can offer a stronger solution to the class imbalance problem, 4) the effect of imbalance at the meta-dataset level is less significant than the effect at the task level with similar imbalance magnitude. The code to reproduce the experiments is released under an open-source license.

翻译：在标准设置中,我们广泛比较了10多个最先进的微小学习和元学习方法,使用了不平衡的任务和元数据集。我们使用迷你-ImagiNet进行的分析显示,1)与平衡的任务相比,类平衡任务对应方的业绩总是下降,优化方法的绩效为18.0美元,衡量方法达到8.4美元;2)与流行信仰相反,元学习算法,如MAML,不自动地学习更均衡,在升级阶段,在升级阶段,通过学习一种更稳定的方法,在升级阶段,可以降低不平衡程度。