2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.HC2024
A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment
Fei Wang, Haoyu Liu, Haoyang Bi +9
For the purpose of efficient and cost-effective large-scale data labeling, crowdsourcing is increasingly being utilized. To guarantee the quality of data labeling, multiple annotat…
cs.HC2023
Towards Long-term Annotators: A Supervised Label Aggregation Baseline
Haoyu Liu, Fei Wang, Minmin Lin +6
Relying on crowdsourced workers, data crowdsourcing platforms are able to efficiently provide vast amounts of labeled data. Due to the variability in the annotation quality of crow…
cs.LG2023★ 2 cited
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
Renyu Zhu, Haoyu Liu, Runze Wu +4
In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambigui…