In the real world, large-scale datasets for visual recognition typically exhibit a long-tailed
distribution, where only a few classes contain adequate samples but the others have (much) …

Traditional federated optimization methods perform poorly with heterogeneous data (ie,
accuracy reduction), especially for highly skewed data. In this paper, we investigate the label …

The heavy reliance on data is one of the major reasons that currently limit the development
of deep learning. Data quality directly dominates the effect of deep learning models, and the …

One-stage long-tailed recognition methods improve the overall performance in a" seesaw"
manner, ie, either sacrifice the head's accuracy for better tail classification or elevate the …

The networks trained on the long-tailed dataset vary remarkably, despite the same training
settings, which shows the great uncertainty in long-tailed learning. To alleviate the …

Prediction of enzyme kinetic parameters is essential for designing and optimizing enzymes
for various biotechnological and industrial applications, but the limited performance of …

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 …

Computer Vision (CV) has achieved remarkable results, outperforming humans in several
tasks. Nonetheless, it may result in significant discrimination if not handled properly. Indeed …

Cell images, which have been widely used in biomedical research and drug discovery,
contain a great deal of valuable information that encodes how cells respond to external …

Learning unbiased node representations under class-imbalanced graph data is challenging
due to interactions between adjacent nodes. Existing studies have in common that they …