Real-world training data usually exhibits long-tailed distribution, where
several majority classes have a significantly larger number of samples than the
remaining minority classes. This imbalance degrades the performance of typical
supervised learning algorithms designed for balanced training sets. In this
paper, we address this issue by augmenting minority classes with a recently
proposed implicit semantic data augmentation (ISDA) algorithm, which produces
diversified augmented samples by translating deep features along many
semantically meaningful directions. Importantly, given that ISDA estimates the
class-conditional statistics to obtain semantic directions, we find it
ineffective to do this on minority classes due to the insufficient training
data. To this end, we propose a novel approach to learn transformed semantic
directions with meta-learning automatically. In specific, the augmentation
strategy during training is dynamically optimized, aiming to minimize the loss
on a small balanced validation set, which is approximated via a meta update
step. Extensive empirical results on CIFAR-LT-10/100, ImageNet-LT, and
iNaturalist 2017/2018 validate the effectiveness of our method.

本文提出一种基于 ISDA 算法的新方法，通过元学习自动学习转换的语义方向来扩充极少数的分类数据样本，以达到解决单纯平衡训练集情况下典型有监督学习算法表现下降问题的目的，并在 CIFAR-LT-10 / 100、ImageNet-LT 和 iNaturalist 2017/2018 数据集上进行实验验证。