This document discusses anomaly detection using one-class neural networks (OC-NN). It begins by introducing one-class support vector machines (OC-SVM) which learn a decision boundary to distinguish normal data points from anomalies using only normal data for training. The document then presents OC-NN as an alternative, where a neural network is trained to learn a low-dimensional representation of only normal data, and anomalies are detected as points with a large reconstruction error. It evaluates OC-NN on several datasets, finding it can achieve good performance compared to OC-SVM at detecting anomalies, as measured by the area under the ROC curve metric.

1. :ANOMALY DETECTION USING
ONE-CLASS NEURAL NETWORKS
Katsuki Ohto @ NN #7 2018/11/5

3.
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4. SVM
One Class SVM
One Class Neural Network (OC-NN)

5. :SVM
One Class Support Vector Machine (OC-SVM)
positive
negative

6. :SVM
2
https://medium.com/deep-math-machine-learning-ai/chapter-3-support-vector-
machine-with-math-47d6193c82be

7. :OC-SVM
One Class Support Vector Machine (OC-SVM)
N X Φ
Φ w →
w

8. OC-SVM
One Class Support Vector Machine (OC-SVM)
w → negative
positive
ν

9. : OC-NN
One Class Neural Network (OC-NN)
OC-SVM : 1.
2.
OC-SVM

10. OC-NN OC-D-SVDD
OC-NN
OC-Deep SVDD ( OC )
OC-NN SVM

11. OC-NN :
w,V r
w,V Back Propagation
r : NN quantile

12. Synthetic DataSets
MNIST, CIFAR-10, USPS ... Convolution AE
PFAM : ... LSTM AE
→
Activation: Linear, Sigmoid, Relu

13. PFAM
normal anomaly

14. PFAM
AUROC (Area Under ROC Curve)

15.
OC-NN T-SNE

16. :
DNN
Robust Deep AutoEncoder
X L S