Although deep neural networks achieve tremendous success on various classification tasks,
the generalization ability drops sheer when training datasets exhibit long-tailed distributions …

A well-trained deep neural network on balanced datasets usually exhibits the Neural
Collapse (NC) phenomenon, which is an informative indicator of the model achieving good …

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

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 tasks are universally associated with training samples that exhibit a long-tailed
class distribution, and traditional deep learning models are not suitable for fitting this …

The real-world data distribution is essentially long-tailed, which poses a significant
challenge to the deep model. Classification models minimizing cross-entropy loss struggle …

Real-world data often exhibits a long-tailed distribution, in which head classes occupy most
of the data, while tail classes only have very few samples. Models trained on long-tailed …

Real-world data are long-tailed, the lack of tail samples leads to a significant limitation in the
generalization ability of the model. Although numerous approaches of class re-balancing …

Long-tailed classifications make it very challenging to deal with class-imbalanced problems
using deep convolutional neural networks (CNNs). Existing solutions based on re-balancing …

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 …