TensorFlow implementation of Anomaly Detection with Adversarial Dual Autoencoders (ADAE) with MNIST dataset.
The Keras implementation is provided as the following link.
https://github.com/kjm1559/ADAE_LSTM_Autoencoder
Loss graphs in the training procedure.
Each graph shows the generative loss, and the two terms that make loss-G.
Loss graphs in the training procedure.
Each graph shows the discriminative loss, and the two terms that make loss-G.
Normal samples classified as normal.
Abnormal samples classified as normal.
Normal samples classified as abnormal.
Abnormal samples classified as abnormal.
- Python 3.7.4
- Tensorflow 1.14.0
- Numpy 1.17.1
- Matplotlib 3.1.1
- Scikit Learn (sklearn) 0.21.3
[1] Ha Son Vu et al. (2019). Anomaly Detection with Adversarial Dual Autoencoders. arXiv preprint arXiv:1902.06924.