Dual-Primal Graph VAEs for Noisy Label Aggregation
Inferring the ground-truth from noisy crowdsourced labels is an important theoretical and practical problem. Neural network-based methods offer an alternative to classical Bayesian models which require specifying a family of generative models used for inference. However, current models either still rely on fairly simple generative models for inference or require pseudo-labels or synthetic data to train the aggregate classifier. We propose a graph VAE architecture in which the decoder and encoder use GAT-based message passing on the adjacency graph of a crowdsourced dataset and its dual, respectively. The ground-truth labels are treated as latent variables, enabling unsupervised representa...
| Authors | Patrick Stinson, Nikolaus Kriegeskorte |
|---|---|
| Primary category | cs.LG |
| Published | 11 August 2026 |
| Last updated at source | 6 October 2026 |
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