An Introduction to Deep Long-Tailed Learning

This survey by Yifan Zhang, Bingyi Kang, Bryan Hooi, Shuicheng Yan and Jiashi Feng covers the following topic in far grater detail and I highly recommend checking it out for a more thorough discussion the ideas discussed in this article. With the massive success of Deep Learning in the field of image recognition comes the need to apply these techniques to solve real-world problems. An issue that arises here, however, is that in real world applications, training samples typically a long-tailed class distribution, where a small portion of classes have massive sample points but the others are associated with only a few samples. Thus, a model can be easily biased towards the head classes, resulting in a poor performance on the tail classes [1]. Many methods to counter such class imbalances in the data have been studied, mostly grouped within 3 categories.