从人群中进行深度学习
Deep learning from crowds
摘要：
在过去的几年里，深度学习通过极大地提高各个领域的最新技术水平，彻底改变了机器学习领域。然而，随着监督人工神经网络的大小增长，通常对更大的标记数据集的需求也会增长。最近，众包已经确立了自己作为一个有效的和具有成本效益的解决方案，以可扩展的方式标记大数据集，但它往往需要聚合标签从多个嘈杂的贡献者与不同水平的专业知识。在本文中，我们解决了从人群中学习深度神经网络的问题。我们首先描述一个期望最大化算法，用于共同学习网络的参数和注释器的可靠性。然后，提出了一种新的通用人群层，它允许我们直接从多个注释器的噪声标签中，仅使用反向传播来训练端到端的深度神经网络。我们的经验表明，所提出的方法能够在内部捕获不同注释器的可靠性和偏差，并在不同设置（即分类，回归和序列标记）中为各种众包数据集实现新的最先进的结果。
英文摘要：
Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the state-of-the-art in various domains. However, as the size of supervised artificial neural networks grows, typically so does the need for larger labeled datasets. Recently, crowdsourcing has established itself as an efficient and cost-effective solution for labeling large sets of data in a scalable manner, but it often requires aggregating labels from multiple noisy contributors with different levels of expertise. In this paper, we address the problem of learning deep neural networks from crowds. We begin by describing an EM algorithm for jointly learning the parameters of the network and the reliabilities of the annotators. Then, a novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels of multiple annotators, using only backpropagation. We empirically show that the proposed approach is able to internally capture the reliability and biases of different annotators and achieve new state-of-the-art results for various crowdsourced datasets across different settings, namely classification, regression and sequence labeling.
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