arXiv:2512. 02653v2 Announce Type: replace Abstract: Multi-view learning integrates diverse representations of the same instances and can improve performance when interactions across views are effectively exploited.
By Farnaz Faramarzi Lighvan, Mehrdad Asadi, Lynn Houthuys
The paper introduces Trusted Multi-view learning with Unified Routing (TMUR), a method that separates view-specific evidence extraction from fusion arbitration in multi-view classification. TMUR employs view-private experts, a collaborative expert, and a unified router that assigns sample-level weights based on global context, along with soft load-balancing and diversity regularization to promote balanced and discriminative expert use. Experiments on 14 datasets show that TMUR consistently improves classification accuracy and reliability compared to 15 recent baselines.
By Yilin Zhang, Cai Xu, Haishun Chen, Ziyu Guan, Wei Zhao
arXiv:2607. 18465v1 Announce Type: new Abstract: Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video.
By Ju Chen, Sijia Xu, Jun Feng, Zhiqiang Gao, Zhengyi Yang
Crowdsourced labeling provides valuable labeled data for domains across natural language processing, computer vision, and video. Label aggregation aims to infer latent true labels from noisy and biased annotations, with the key lying in annotator reliability estimation.
arXiv:2606. 14965v1 Announce Type: new Abstract: Synthetic instance-dependent label noise (IDN) benchmarks are widely used to evaluate noisy-label learning methods, yet existing approaches typically generate noise through imperfect annotators or classifier raters, leaving the source of ambiguity implicit.
By Shadman Islam, Agustinus Kristiadi, Mostafa Milani
arXiv:2608. 03432v1 Announce Type: new Abstract: Refurbishment-based noisy-label learning mixes an observed label with a model-derived pseudo target, typically using one sample-wise cleanliness score to control both branches.
By Wenxiao Fan, Kan Li