Real-world data often follow a long-tailed distribution as the frequency of each class is
typically different. For example, a dataset can have a large number of under-represented …

With the rapid increase of large-scale, real-world datasets, it becomes critical to address the
problem of long-tailed data distribution (ie, a few classes account for most of the data, while …

Imbalanced datasets with long-tailed distribution widely exist in practice, posing great
challenges for deep networks on how to handle the biased predictions between head …

Large-scale data from real-world usually follow a long-tailed distribution (ie, a few majority
classes occupy plentiful training data, while most minority classes have few samples) …

Class imbalance problems frequently occur in real-world tasks, and conventional deep
learning algorithms are well known for performance degradation on imbalanced training …

Real-world data is often unbalanced and long-tailed, but deep models struggle to recognize
rare classes in the presence of frequent classes. To address unbalanced data, most studies …

The long-tail distribution of the visual world poses great challenges for deep learning based
classification models on how to handle the class imbalance problem. Existing solutions …

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 …

Long-tail learning has received significant attention in recent years due to the challenge it
poses with extremely imbalanced datasets. In these datasets, only a few classes (known as …

Real-world data often follows a long-tailed distribution, which makes the performance of
existing classification algorithms degrade heavily. A key issue is that the samples in tail …