Variational lossy autoencoder

# Variational lossy autoencoder Source: [https://openai.com/index/variational-lossy-autoencoder/](https://openai.com/index/variational-lossy-autoencoder/) ## Abstract 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\. In addition, by leveraging autoregressive models as both prior distribution p\(z\) and decoding distribution p\(x\|z\), we can greatly improve generative modeling performance of VAEs, achieving new state\-of\-the\-art results on MNIST, OMNIGLOT and Caltech\-101 Silhouettes density estimation tasks\.