Few-Shot Learning with Class Imbalance

[YeonwooSung]

E. Triantafillou et. al. [1] had experiments for few-shot learning with class imbalance to see if the class imbalance actually impacts to the performance of the few-shot learning methods.

Results

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.

Reference

[1] Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, Hugo Larochelle. Few-Shot Learning with Class Imbalance

[YeonwooSung]

What is meta-dataset? Then you should read the paper below.

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples