The choice of loss functions, despite being a critical factor when training ... Specifically, we propose a Local Residual Quantized Variational AutoEncoder (Local RQ-VAE) to learn prototype vectors ...

It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data ...

To tackle these challenges, in this paper, we propose a unified Unsupervised Gaussian Mixture Variational Autoencoder for outlier detection. Specifically, a variational autoencoder firstly trains a ...