论文标题
Neucrowd：具有众包标签的代表性学习的神经抽样网络
NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels
论文作者
论文摘要
代表性学习方法需要大量的判别培训数据，这在许多情况下都无法使用，例如医疗保健，智慧城市，教育等。实际上，人们是指众包获取注释标签。但是，由于数据隐私，预算限制，特定于域特异性注释者之类的问题，众包标签的数量仍然非常有限。此外，由于注释者的多样化专业知识，众包标签通常是不一致的。因此，直接应用现有的监督表示学习（SRL）算法可能很容易获得过度拟合的问题并产生次优的解决方案。在本文中，我们提出了\ emph {neucrowd}，这是一个来自众包标签的SRL的统一框架。提出的框架（1）通过利用安全意识的采样和强大的锚定生成来创建足够数量的高质量\ emph {n} -tuplet训练样品； （2）自动学习一个自适应学习以选择SRL网络的有效样本的神经采样网络。在一个合成和三个现实世界数据集上评估了所提出的框架。结果表明，就预测准确性和AUC而言，我们的方法的表现优于广泛的最新基准。为了鼓励可重现的结果，我们在\ url {https://github.com/tal-ai/neucrowd_kais2021}上公开代码。
Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc. In practice, people refer to crowdsourcing to get annotated labels. However, due to issues like data privacy, budget limitation, shortage of domain-specific annotators, the number of crowdsourced labels is still very limited. Moreover, because of annotators' diverse expertise, crowdsourced labels are often inconsistent. Thus, directly applying existing supervised representation learning (SRL) algorithms may easily get the overfitting problem and yield suboptimal solutions. In this paper, we propose \emph{NeuCrowd}, a unified framework for SRL from crowdsourced labels. The proposed framework (1) creates a sufficient number of high-quality \emph{n}-tuplet training samples by utilizing safety-aware sampling and robust anchor generation; and (2) automatically learns a neural sampling network that adaptively learns to select effective samples for SRL networks. The proposed framework is evaluated on both one synthetic and three real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage reproducible results, we make our code publicly available at \url{https://github.com/tal-ai/NeuCrowd_KAIS2021}.
