J Gao, H Zhao, D dan Guo, H Zha - Forty-first International …, 2024 - openreview.net
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 …
S Ahn, J Ko, SY Yun - arXiv preprint arXiv:2302.05499, 2023 - arxiv.org
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 …
B Wang, P Wang, W Xu, X Wang, Y Zhang… - The Twelfth …, 2024 - openreview.net
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 …
X Xiang, Z Zhang, X Chen - Neurocomputing, 2024 - Elsevier
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 …
Q Chen, T Huang, G Zhu, E Lin - Neural Networks, 2023 - Elsevier
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 …
Y Ma, L Jiao, F Liu, S Yang, X Liu, P Chen - International Journal of …, 2024 - Springer
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 …
Z Li, H Zhao, Y Lin - Information Sciences, 2022 - Elsevier
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 …