Robust Vector Quantized-Variational Autoencoder

Understand

Image generative models can learn the distributions of the training data and consequently generate examples by sampling from these distributions.

• However, when the training dataset is corrupted with outliers, generative models will likely produce examples that are also similar to the outliers.
• In fact, a small portion of outliers may induce state-of-the-art generative models, such as Vector Quantized-Variational AutoEncoder (VQ-VAE), to learn a significant mode from the outliers.
• To mitigate this problem, we propose a robust generative model based on VQ-VAE, which we name Robust VQ-VAE (RVQ-VAE).