arXiv:2512.12870v2 Announce Type: replace-cross
Abstract: Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are of...
By Pouya Ahadi, Blair Winograd, Camille Zaug, Karunesh Arora, Lijun Wang, Kamran Paynabar
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
The paper investigates how two signals—input‑conditional uncertainty and prediction‑label loss—detect different types of data corruption in federated learning. Experiments on ResNet‑20 with CIFAR‑10 and SVHN show that prediction‑label loss excels at spotting persistent random label flips, while expected‑entropy uncertainty better identifies additive image noise. The authors argue that effective federated data‑quality assessment must match the chosen signal to the specific corruption type rather than rely solely on uncertainty measures.
By Bradley Scott, Zeqi Luo, Edmond S. L. Ho
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
arXiv:2609.16380v1 Announce Type: new
Abstract: Class-balanced learning and label noise create a coupled failure mode: frequency correction prevents majority classes from dominating the decision rule...
By Mushir Akhtar, Akarsh J., M. Tanveer, Mohd. Arshad
arXiv:2606. 11695v1 Announce Type: cross Abstract: High-quality labeled data is essential for training reliable ML/DL models.
By Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La, Phong Lam, Thu-Trang Nguyen, Son Nguyen, Hieu Dinh Vo