  Many data distributions in the real world are hardly uniform. Instead, skewed
and long-tailed distributions of various kinds are commonly observed. This
poses an interesting problem for machine learning, where most algorithms assume
or work well with uniformly distributed data. The problem is further
exacerbated by current state-of-the-art deep learning models requiring large
volumes of training data. As such, learning from imbalanced data remains a
challenging research problem and a problem that must be solved as we move
towards more real-world applications of deep learning. In the context of class
imbalance, state-of-the-art (SOTA) accuracies on standard benchmark datasets
for classification typically fall less than 75%, even for less challenging
datasets such as CIFAR100. Nonetheless, there has been progress in this niche
area of deep learning. To this end, in this survey, we provide a taxonomy of
various methods proposed for addressing the problem of long-tail
classification, focusing on works that happened in the last few years under a
single mathematical framework. We also discuss standard performance metrics,
convergence studies, feature distribution and classifier analysis. We also
provide a quantitative comparison of the performance of different SOTA methods
and conclude the survey by discussing the remaining challenges and future
research direction.
