Deep Learning Classification With Noisy Labels
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2020
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866910600061255680 |
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| author | Sanchez, Guillaume Guis, Vincente Marxer, Ricard Bouchara, Frédéric |
| author_facet | Sanchez, Guillaume Guis, Vincente Marxer, Ricard Bouchara, Frédéric |
| contents | Deep Learning systems have shown tremendous accuracy in image classification, at the cost of big image datasets. Collecting such amounts of data can lead to labelling errors in the training set. Indexing multimedia content for retrieval, classification or recommendation can involve tagging or classification based on multiple criteria. In our case, we train face recognition systems for actors identification with a closed set of identities while being exposed to a significant number of perturbators (actors unknown to our database). Face classifiers are known to be sensitive to label noise. We review recent works on how to manage noisy annotations when training deep learning classifiers, independently from our interest in face recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2004_11116 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Deep Learning Classification With Noisy Labels Sanchez, Guillaume Guis, Vincente Marxer, Ricard Bouchara, Frédéric Machine Learning Deep Learning systems have shown tremendous accuracy in image classification, at the cost of big image datasets. Collecting such amounts of data can lead to labelling errors in the training set. Indexing multimedia content for retrieval, classification or recommendation can involve tagging or classification based on multiple criteria. In our case, we train face recognition systems for actors identification with a closed set of identities while being exposed to a significant number of perturbators (actors unknown to our database). Face classifiers are known to be sensitive to label noise. We review recent works on how to manage noisy annotations when training deep learning classifiers, independently from our interest in face recognition. |
| title | Deep Learning Classification With Noisy Labels |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2004.11116 |