Deep neural networks usually perform poorly when the training dataset suffers from extreme
class imbalance. Recent studies found that directly training with out-of-distribution data (ie …

Real-world data universally confronts a severe class-imbalance problem and exhibits a long-
tailed distribution, ie, most labels are associated with limited instances. The naïve models …

The problem of class imbalanced data is that the generalization performance of the classifier
deteriorates due to the lack of data from minority classes. In this paper, we propose a novel …

Deep neural networks may perform poorly when training datasets are heavily class-
imbalanced. Recently, two-stage methods decouple representation learning and classifier …

Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …

Real-world data typically follow a long-tailed distribution, where a few majority categories
occupy most of the data while most minority categories contain a limited number of samples …

This paper considers learning deep features from long-tailed data. We observe that in the
deep feature space, the head classes and the tail classes present different distribution …

Long-tailed learning aims to tackle the crucial challenge that head classes dominate the
training procedure under severe class imbalance in real-world scenarios. However, little …

Deep learning algorithms face great challenges with long-tailed data distribution which,
however, is quite a common case in real-world scenarios. Previous methods tackle the …

Existing long-tailed classification (LT) methods only focus on tackling the class-wise
imbalance that head classes have more samples than tail classes, but overlook the attribute …