arXiv:2607. 22258v1 Announce Type: new Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks.
By Yi-Hang Zhu, Rajeev Raman, Shiqi Su, Jianyuan Sun, Xinyu Yang, Nan Xing, Huiyu Zhou
arXiv:2609.16365v1 Announce Type: cross
Abstract: Real-world datasets often exhibit long-tailed class distributions, where a few head classes contain a large number of training samples while a large...
By Siyu Yuan
arXiv:2607.22258v2 Announce Type: replace
Abstract: Models trained on long-tailed data using standard softmax tend to exhibit higher training error and a larger generalisation gap for classes with fe...
By Yi-Hang Zhu, Rajeev Raman, Shiqi Su, Jianyuan Sun, Xinyu Yang, Nan Xing, Huiyu Zhou
arXiv:2609.37331v1 Announce Type: new
Abstract: Balancing performance trade-offs on long-tailed data distributions remains a long-standing challenge in visual recognition. Existing methods mainly imp...
By Shenghan Chen, Yiming Liu, Zhipeng Deng, Haolin Wang, Jiale Zhou, Zhijian Wu, Xiankai Lu, Yafei Ou, Yefeng Zheng
arXiv:2609.00093v1 Announce Type: new
Abstract: Imbalanced time series classification is often addressed by changing the training distribution, objective, logits, or final threshold. These interventi...
By Chuanhang Qiu, Yanran Xu, Yue Wang, Anthony Bagnall
arXiv:2608.30699v1 Announce Type: cross
Abstract: Long-tailed distributions are prevalent in real-world semi-supervised learning (SSL), where pseudo-labels tend to favor majority classes, leading to...
By Yue Cheng, Jiajun Zhang, Xiaohui Gao, Weiwei Xing, Zhanxing Zhu