ConNet: Deep Semi-Supervised Anomaly Detection Based on Sparse Positive Samples
Existing semi-supervised anomaly detection methods usually use a large amount of labeled normal data for training, which have the problem of high labeling costs. Only a few semi-supervised methods utilize unlabeled data and a few labeled anomalies to train models. However, these kinds of methods usu...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9420659/ |