Popular deep learning architectures that can be used in an anomaly detection framework include: Autoencoder ... Recurrent Neural Network (e.g. LSTM, GRU, Attention), however, can handle temporal ...

The demo program presented in this article uses image data, but the autoencoder anomaly detection technique can work with any type of data. The demo begins by creating a Dataset object that stores the ...

In the paper, the researchers propose TadGAN: an “unsupervised anomaly detection approach built on Generative ... six deep learning methods (HTM, LSTM, LSTM AutoEncoder, MAD-GAN, Microsoft Azure ...

Therefore, the autoencoder input and output both have 65 values -- 64 pixel grayscale values (0 to 16) plus a label (0 to 9). Notice that the demo program analyzes both the predictors (pixel values) ...