Scalpel-CD: Leveraging Crowdsourcing and Deep Probabilistic Modeling for Debugging Noisy Training Data

Jie Yang, Alisa Smirnova, Dingqi Yang, Gianluca Demartini, Yuan Lu, Philippe Cudré-Mauroux

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

Abstract

This paper presents Scalpel-CD, a first-of-its-kind system that leverages both human and machine intelligence to debug noisy labels from the training data of machine learning systems. Our system identifies potentially wrong labels using a deep probabilistic model, which is able to infer the latent class of a high-dimensional data instance by exploiting data distributions in the underlying latent feature space. To minimize crowd efforts, it employs a data sampler which selects data instances that would benefit the most from being inspected by the crowd. The manually verified labels are then propagated to similar data instances in the original training data by exploiting the underlying data structure, thus scaling out the contribution from the crowd. Scalpel-CD is designed with a set of algorithmic solutions to automatically search for the optimal configurations for different types of training data, in terms of the underlying data structure, noise ratio, and noise types (random vs. structural). In a real deployment on multiple machine learning tasks, we demonstrate that Scalpel-CD is able to improve label quality by 12.9% with only 2.8% instances inspected by the crowd.
Original languageEnglish
Title of host publicationWWW '19: The World Wide Web Conference
PublisherAssociation for Computing Machinery (ACM)
Pages2158–2168
ISBN (Electronic)978-1-4503-6674-8
Publication statusPublished - 2019
Externally publishedYes

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