This is the official codebase for our 2022 HCOMP paper:
Katherine M. Collins*, Umang Bhatt*, and Adrian Weller
*Contributed equally
Paper: https://arxiv.org/abs/2207.00810
Project Page: https://sites.google.com/view/eliciting-individ-soft-labels
The labels used to train machine learning (ML) models are of paramount importance. Typically for ML classification tasks, datasets contain hard labels, yet learning using soft labels has been shown to yield benefits for model generalization, robustness, and calibration. Earlier work found success in forming soft labels from multiple annotators' hard labels; however, this approach may not converge to the best labels and necessitates many annotators, which can be expensive and inefficient. We focus on efficiently eliciting soft labels from individual annotators. We collect and release a dataset of soft labels for CIFAR-10 via a crowdsourcing study (N=248). We demonstrate that learning with our labels achieves comparable model performance to prior approaches while requiring far fewer annotators. Our elicitation methodology therefore shows promise towards enabling practitioners to enjoy the benefits of improved model performance and reliability with fewer annotators, and serves as a guide for future dataset curators on the benefits of leveraging richer information, such as categorical uncertainty, from individual annotators.
The CIFAR-10S dataset is included in the cifar10s_data directory. Details are included in the README in the directory.
We download the CIFAR-10H data from the authors' fantastic repository.
We store CIFAR-10H and other aspects of the data for the computational experiments, e.g., the split train/test indices, in other_data.
Code to run the computational experiments is included in the computational_experiments directory.
We include details of the elicitation interface in elicitation_interface directory. Note, this directory could be readily uploaded to form a new Pavlovia project (which naturally integrates with GitLab). Altnernatively, you could download the directory and host an experimental interface/server locally.
If citing us, please consider the following bibtex entry:
@inproceedings{softLabelElicitingLearning2022,
title={Eliciting and Learning with Soft Labels from Every Annotator},
author={Collins, Katherine M and Bhatt, Umang and Weller, Adrian},
booktitle={Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (HCOMP)},
volume={10},
year={2022}
}
If you have any questions or issues with any aspect of our repository, please feel free to instantiate a GitHub Issue, or reach out to us at kmc61@cam.ac.uk