Generative Models for Learning from Crowds
arXiv:1706.03930v3
Originality Incremental advance
AI Analysis
This addresses the challenge of aggregating noisy labels from multiple annotators, which is crucial for improving data quality in machine learning applications, but appears incremental in approach.
The paper tackles the problem of label aggregation from crowds by proposing generative probabilistic models, achieving consistent outperformance over state-of-the-art methods.
In this paper, we propose generative probabilistic models for label aggregation. We use Gibbs sampling and a novel variational inference algorithm to perform the posterior inference. Empirical results show that our methods consistently outperform state-of-the-art methods.