The document discusses deep semi-supervised anomaly detection, focusing on different methodologies such as deep one-class classification and autoencoders. It mentions loss calculation and compares various datasets like MNIST and CIFAR-10 in the context of anomaly detection. The content emphasizes the application of semi-supervised techniques in improving detection accuracy.

1. 2020/6/14
Yamato OKAMOTO
ICLR
Deep Semi-supervised Anomaly Detection

2. !!
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Twitter : RoadRoller_DESU
Anomaly Detection

3. Anomaly Detection
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• What is
• ※
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• What is
• What is Not

4. Anomaly Detection
Deep One-Class Classification (ICML’18)
• Classifier AutoEncoder
• LOSS
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c ≠0
n

5. Anomaly Detection
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• 10
10
ad-hoc post-hoc
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• LOSS OK
• post-hoc

6. Semi-supervised
Anomaly Detection Unsupervised
Semi-supervised
※Semi-supervised Anomaly Detection

7. LOSS
Deep One-Class Classification (ICML’18) LOSS Semi-supervised
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m semi-supervised
yj or

8. HigherisBetter
Unsupervised Semi-supervised
Semi-supervised
MNIST Fashion-MNIST CIFAR-10
• AutoEncoder
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9. Semi-supervised Anomaly Detection
• Anomaly Detection semi-supervised
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• post-hoc
• AE Classification
• Anomaly Detection
MNIST