Machine Learning with Noisy Labels Definitions, Theory, Techniques and Solutions
Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling process involved in the acquisition of such training sets limits the number and size of...
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
San Diego :
Elsevier Science & Technology
2024.
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| Edición: | 1st ed |
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| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835404506719 |
| Sumario: | Most of the modern machine learning models, based on deep learning techniques, depend on carefully curated and cleanly labelled training sets to be reliably trained and deployed. However, the expensive labelling process involved in the acquisition of such training sets limits the number and size of datasets available to build new models, slowing down progress in the field. Alternatively, many poorly curated training sets containing noisy labels are readily available to be used to build new models. However, the successful exploration of such noisy-label training sets depends on the development of algorithms and models that are robust to these noisy labels. |
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| Descripción Física: | 1 online resource (314 pages) |
| ISBN: | 9780443154423 9780443154416 |
