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Semi-Supervised Learning for Defect Segmentation with Autoencoder Auxiliary Module
In general, one may have access to a handful of labeled normal and defect datasets. Most unlabeled datasets contain normal samples because the defect samples occurred rarely. Thus, the majority of approaches for anomaly detection are formed as unsupervised problems. Most of the previous methods have...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030561/ https://www.ncbi.nlm.nih.gov/pubmed/35458900 http://dx.doi.org/10.3390/s22082915 |