A GAN-based anomaly detector using multi-feature fusion and selection
Abstract In numerous applications, abnormal samples are hard to collect, limiting the use of well-established supervised learning methods. GAN-based models which trained in an unsupervised and single feature set manner have been proposed by simultaneously considering the reconstruction error and the...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-52378-9 |