Deep learning from crowds

TL;DR

This paper introduces a novel end-to-end deep learning approach that effectively learns from noisy, crowdsourced labels by modeling annotator reliability, achieving state-of-the-art results across multiple tasks.

Contribution

It proposes a new crowd layer for deep networks and an EM algorithm to jointly learn network parameters and annotator reliabilities from noisy labels.

Findings

Achieves state-of-the-art results on crowdsourced datasets

Effectively models annotator reliability and biases

Works across classification, regression, and sequence labeling

Abstract

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.