arXiv:2607. 08109v1 Announce Type: new Abstract: We propose contrastive order learning (ConOrd), a contrastive learning framework for ordinal regression that integrates the strengths of contrastive learning and order learning.
By Chaewon Lee, BeomJun Shim, Kwang Pyo Choi, Chang-Su Kim
arXiv:2607. 23575v1 Announce Type: cross Abstract: Ordinal regression is widely used in scenarios where labels are discrete yet inherently ordered.
By Chunlai Dong, Yaojun Hu, Yuyang Xu, Haochao Ying, Jian Wu
arXiv:2608. 04652v1 Announce Type: cross Abstract: Image mixup is a widely adopted data augmentation strategy, yet it is ill-suited for ordinal classification tasks such as medical disease grading, where labels encode a progression of severity.
By Dileepa Pitawela, Gustavo Carneiro, Hsiang-Ting Chen
arXiv:2607. 08103v1 Announce Type: new Abstract: Rank estimation under label noise poses a fundamental challenge, as ordinal annotations often exhibit structured uncertainty rather than simple label corruption.
By Chaewon Lee, Seon-Ho Lee, Chang-Su Kim
arXiv:2606. 25232v1 Announce Type: new Abstract: Ordered bottlenecks aim to provide utility at flexible budgets by assigning coarse information to early tokens and task-relevant detail to later ones.
By Erik Ayari, Manuel Traub, Martin V. Butz
arXiv:2606. 07599v1 Announce Type: cross Abstract: Ordinal Regression (OR) aims to predict target values with inherent order, underpinning critical applications across diverse domains, from recommender systems to computer vision.
By Hongxu Ma, Lin Wang, Chenghou Jin, Han Zhou, Jie Zhang, Xiaoyu Yang, Chunjie Chen, Jihong Guan, Shuigeng Zhou