用于噪声标签聚合的双原图变分自动编码器
Dual-Primal Graph VAEs for Noisy Label Aggregation
- Zuckerman Institute（扎克曼研究所）
- Columbia University（哥伦比亚大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出双原图变分自动编码器，将真实标签作为潜变量实现无监督学习，在众包基准获最优性能，还可扩充图纳入辅助信息提升分类性能。
AI中文摘要：
从众包噪声标签中推断真实标签是一个重要的理论与实际问题。基于神经网络的方法为经典贝叶斯模型提供了替代方案，后者需要指定用于推断的生成模型族。然而，现有模型要么仍依赖相当简单的生成模型进行推断，要么需要伪标签或合成数据来训练聚合分类器。我们提出一种图变分自动编码器（Graph VAE）架构，其中解码器和编码器分别在众包数据集的邻接图及其对偶图上使用基于图注意力网络（GAT）的消息传递。真实标签被视为潜变量，从而无需训练单独的分类器即可实现无监督表示学习。我们表明，该模型在众包基准测试中达到了最先进的性能。随后，我们通过展示如何扩充原始众包图以纳入辅助信息（例如从基于噪声标签训练的神经网络分类器中得到的表示），来证明我们方法的通用性，这能在测试时大幅提升分类性能。
英文摘要：
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 representation learning without needing to train a separate classifier. We show our model achieves state of the art performance on crowdsourcing benchmarks. We then demonstrate the generality of our approach by showing how the original crowdsourcing graph can be augmented to incorporate side information such as representations from neural network classifiers trained on the noisy labels to substantially boost their classification performance at test time.
