Class Imbalance in Few-Shot Learning

Few-shot learning aims to train models on a limited number of labeled samples from 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. 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: dataset vs. support set 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 methods using backbones of different depths on multiple datasets. Our analysis reveals that 1) compared to the balanced task, the performances of their class-imbalance counterparts always drop, by up to $18.0\%$ for optimization-based methods, although feature-transfer and metric-based methods generally suffer less, 2) strategies used to mitigate imbalance in supervised learning can be adapted to the few-shot case resulting in better performances, 3) the effects of imbalance at the dataset level are less significant than the effects at the support set level. The code to reproduce the experiments is released under an open-source license.