US 12626104 B2 / May 12, 2026

Methods of training variational autoencoders to recognize anomalous data in distributed systems

Methods of training a variational autoencoder (VAE) to recognize anomalous data in a distributed system are provided. Input image data representative of devices/processes in a distributed system are provided to an encoder of a VAE on a processor. The input image data is compressed, via the processor, using a first plurality of weights with the encoder. A normal distribution of the compressed image data is created in a latent space of the VAE. The compressed image data from the latent space is decompressed using a second plurality of weights with a decoder of the VAE. The decompressed image data from the decoder is optimized. At least the first and second plurality of weights are updated, via the processor, based on the loss detected in the optimized decompressed image data. The above steps are iterated until the decompressed image data possesses substantially the same statistical properties as the input image data.