Variational Lossy Autoencoder

Understand

Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification.

• For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture.
• In this paper, we present a simple but principled method to learn such global representations by combining Variational Autoencoder (VAE) with neural autoregressive models such as RNN, MADE and PixelRNN/CNN.
• Our proposed VAE model allows us to have control over what the global latent code can learn and , by designing the architecture accordingly, we can force the global latent code to discard irrelevant information such as texture in 2D images, and hence the VAE only "autoencodes" data in a lossy fashion.