Training deep neural networks usually requires a large amount of labeled data to obtain
good performance. However, in medical image analysis, obtaining high-quality labels for the …

Existing long-tailed recognition methods, aiming to train class-balanced models from long-
tailed data, generally assume the models would be evaluated on the uniform test class …

Deep learning has achieved remarkable progress for visual recognition on large-scale
balanced datasets but still performs poorly on real-world long-tailed data. Previous methods …

Graph Neural Network (GNN) based recommender systems have been attracting more and
more attention in recent years due to their excellent performance in accuracy. Representing …

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 …

The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …

Highly imbalanced datasets are ubiquitous in medical image classification problems. In such
problems, it is often the case that rare classes associated to less prevalent diseases are …

In a typical multi-label setting, a picture contains on average few positive labels, and many
negative ones. This positive-negative imbalance dominates the optimization process, and …

Deep learning techniques have been widely applied to hyperspectral image (HSI)
classification and have achieved great success. However, the deep neural network model …

Long-tailed data is still a big challenge for deep neural networks, even though they have
achieved great success on balanced data. We observe that vanilla training on long-tailed …