Improve learning combining crowdsourced labels by weighting Areas Under the Margin

Tanguy Lefort, Benjamin Charlier, Alexis Joly, Joseph Salmon

Keywords: crowdsourcing, ambiguity, area under the margin, aggregation, noisy labels

TL;DR: We introduced the weighted Areas Under the Margin to identify ambiguous tasks in crowdsourced learning scenarios

Abstract: In supervised learning -- for instance in image classification -- modern massive datasets are commonly labelled by a crowd of workers. The obtained labels in this crowdsourcing setting are then aggregated for training. The aggregation step generally leverages a per worker trust score. Yet, such worker-centric approaches discard each task ambiguity. Some intrinsically ambiguous tasks might even fool expert workers, which could eventually be harmful for the learning step. In a standard supervised learning setting -- with one label per task and balanced classes -- the Area Under the Margin (AUM) statistic is tailored to identify mislabeled data. We adapt the AUM to identify ambiguous tasks in crowdsourced learning scenarios, introducing the Weighted AUM (WAUM). The WAUM is an average of AUMs weighted by worker and task dependent scores. We show that the WAUM can help discarding ambiguous tasks from the training set, leading to better generalization or calibration performance. We report improvements with respect to feature-blind aggregation strategies both for simulated settings and for the CIFAR-10H crowdsourced dataset.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics

Submission Guidelines: Yes

Please Choose The Closest Area That Your Submission Falls Into: General Machine Learning (ie none of the above)

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/improve-learning-combining-crowdsourced/code)