Robust Variational Autoencoder

Understand

Machine learning methods often need a large amount of labeled training data.

• Since the training data is assumed to be the ground truth, outliers can severely degrade learned representations and performance of trained models.
• Here we apply concepts from robust statistics to derive a novel variational autoencoder that is robust to outliers in the training data.
• Variational autoencoders (VAEs) extract a lower-dimensional encoded feature representation from which we can generate new data samples.