deep anomaly detection using geometric transformations

1. Deep Anomaly Detection Using Geometric Transformations
Kang, Min-Guk
Mingukkang1994@gmail.com
March, 17, 2019
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PR-148
https://arxiv.org/abs/1805.10917

2. Contents
1. Methodology(Anomaly Detection)
2. Deep Anomaly Detection using Geometric Transformation
3. Experiments
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3. 3/21
Methodology

4. 4/21
Anomaly Detection One-class classification
Multi-class classification

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Anomaly Detection One-class classification
Multi-class classification
We want to find a dog image between cats.

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Anomaly Detection One-class classification
Multi-class classification
Dog Cat Penguin
?????

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Anomaly Detection One-class classification
Multi-class classification
A baseline for Detecting Misclassified and out-of-distribution examples in neural networks(2017, ICLR)
Training Confidence-calibrated Classifier for Detecting Out-of-Distribution Samples(2018, ICLR)
A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks(2018, NIPS)
A loss framework for calibrated anomaly detection(2018, NIPS)
Deep Anomaly Detection with Outlier Exposure(2019, ICLR)
…
Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery(2017, IPMI)
Adversarially Learned One-class Classifier for Novelty Detection(2018, CVPR)
Deep One-class Classification(2018, ICML)
Deep Autoencoding Gaussian Mixture model for Unsupervised Anomaly Detection(2018, ICLR)
Generative Probabilistic Novelty Detection with Adversarial Autoencoder(2018, NIPS)
…

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Anomaly Detection is widely divided three!
1. Statistical Model(Low density rejection Principle)
Neural
Networks
𝑍1
𝑍2
Scatter

9. 9/21
2. Reconstruction based model
Neural
Networks
𝑍1
𝑍2
Neural
Networks
L1, L2 distance
Anomaly Detection is widely divided three!

10. 10/21
2. Reconstruction based model
Neural
Networks
𝑍1
𝑍2
Neural
Networks
L1, L2 distance
Anomaly Detection is widely divided three!

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3. Kernel based model(DSVDD)
Anomaly Detection is widely divided three!

12. Deep Anomaly Detection using Geometric Transformations
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13. 13/21
Neural
Networks
Create 72 different geometric transfored images
(Translation 9 x Flip 2 x Rotation 4 = 72)
…
Trans(Images)
…
Pred
…
gt
0
1
0
0
0
MSE(Pred, gt)

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Neural
Networks
Create 72 different geometric transfored images
(Translation 9 x Flip 2 x Rotation 4 = 72)
…
…
Pred
0.612
0.005
0.001
Simple version:
Use the sum of maximum softmax probabilities
as an anomaly score!

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Neural
Networks
Create 72 different geometric transfored images
(Translation 9 x Flip 2 x Rotation 4 = 72)
…
…
Pred
0.002
0.575
0.102
Simple version:
Use the sum of maximum softmax probabilities
as an anomaly score!

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Neural
Networks
Create 72 different geometric transfored images
(Translation 9 x Flip 2 x Rotation 4 = 72)
…
…
Pred
Simple version:
Use the sum of maximum softmax probabilities
as an anomaly score!
0.002
0.005
0.710

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Create 72 different geometric transfored images
(Translation 9 x Flip 2 x Rotation 4 = 72)
…
Softmax probability vectors
Complicated version:
fit a dirichlet probability distribution to softmax
probabilities of training image.
And use low density rejection principle!… … … …
Summation of column elements is 1.

18. Experiments
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19. 19/21https://www.cs.toronto.edu/~kriz/cifar.html
Normal samples(training data)
Test samples(test data)
:1000 *10 images
In this paper, pure version of anomaly detection was performed.

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21. Thank you!
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