Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909191075004416 |
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| author | Herde, Marek Lührs, Lukas Huseljic, Denis Sick, Bernhard |
| author_facet | Herde, Marek Lührs, Lukas Huseljic, Denis Sick, Bernhard |
| contents | Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that, typically, multiple annotators, e.g., crowdworkers, provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eight state-of-the-art approaches on eleven datasets with noisy class labels provided either by human or simulated annotators. Our code is publicly available through our repository at https://github.com/ies-research/annot-mix. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03386 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension Herde, Marek Lührs, Lukas Huseljic, Denis Sick, Bernhard Machine Learning I.2.6; I.5.1 Training with noisy class labels impairs neural networks' generalization performance. In this context, mixup is a popular regularization technique to improve training robustness by making memorizing false class labels more difficult. However, mixup neglects that, typically, multiple annotators, e.g., crowdworkers, provide class labels. Therefore, we propose an extension of mixup, which handles multiple class labels per instance while considering which class label originates from which annotator. Integrated into our multi-annotator classification framework annot-mix, it performs superiorly to eight state-of-the-art approaches on eleven datasets with noisy class labels provided either by human or simulated annotators. Our code is publicly available through our repository at https://github.com/ies-research/annot-mix. |
| title | Annot-Mix: Learning with Noisy Class Labels from Multiple Annotators via a Mixup Extension |
| topic | Machine Learning I.2.6; I.5.1 |
| url | https://arxiv.org/abs/2405.03386 |