https://github.com/yeonghyeon/cvae-anomalydetection-pytorch
Example of Anomaly Detection using Convolutional Variational Auto-Encoder (CVAE)
https://github.com/yeonghyeon/cvae-anomalydetection-pytorch
anomaly-detection convolutional-neural-networks generative-neural-network mnist-dataset pytorch variational-autoencoder
Last synced: 6 months ago
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Example of Anomaly Detection using Convolutional Variational Auto-Encoder (CVAE)
- Host: GitHub
- URL: https://github.com/yeonghyeon/cvae-anomalydetection-pytorch
- Owner: YeongHyeon
- Created: 2019-11-07T10:32:01.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-04-09T17:38:24.000Z (about 1 year ago)
- Last Synced: 2024-04-09T19:36:13.729Z (about 1 year ago)
- Topics: anomaly-detection, convolutional-neural-networks, generative-neural-network, mnist-dataset, pytorch, variational-autoencoder
- Language: Python
- Homepage:
- Size: 2.31 MB
- Stars: 36
- Watchers: 5
- Forks: 2
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
[PyTorch] Anomaly Detection using Convolutional Variational Auto-Encoder (CVAE)
=====
Example of Anomaly Detection using Convolutional Variational Auto-Encoder (CVAE) [TensorFlow 1.x] [TensorFlow 2.x].
## Architecture
Simplified VAE architecture.
## Problem Definition
'Class-1' is defined as normal and the others are defined as abnormal.
## Results
||MNIST|Fashion-MNIST|
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|Reconstruciton of training|
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|Latent of training|
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|Latent walk|
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|Latent of test|
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|Histogram of test|
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|AUROC|0.997|0.980|
## Environment
* Python 3.7.4
* PyTorch 1.1.0
* Numpy 1.17.1
* Matplotlib 3.1.1
* Scikit Learn (sklearn) 0.21.3
## Reference
[1] Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
[2] Kullback Leibler divergence. Wikipedia