#variational autoencoders

[artifical-intelligence-services]

VAE for Anomaly Detection

Variational Autoencoders (VAEs) are powerful tools for generating data, especially useful for data augmentation and spotting anomalies. By working with latent spaces, VAEs help to diversify datasets and capture complex data patterns, making them particularly effective at identifying outliers. Advanced versions, like Conditional VAEs and Beta-VAEs, further enhance data generation and improve model performance. With their ability to handle complex data, VAEs are making a big impact in AI, offering innovative solutions across various fields. Read the full article here

[alyda]

Decoded averaged latent vectors of lantern drawings from different countries (using sketch-rnn variational autoencoder on Google Quick, Draw! data) 

January 2018