  While autoregressive models excel at image compression, their sample quality
is often lacking. Although not realistic, generated images often have high
likelihood according to the model, resembling the case of adversarial examples.
Inspired by a successful adversarial defense method, we incorporate randomized
smoothing into autoregressive generative modeling. We first model a smoothed
version of the data distribution, and then reverse the smoothing process to
recover the original data distribution. This procedure drastically improves the
sample quality of existing autoregressive models on several synthetic and
real-world image datasets while obtaining competitive likelihoods on synthetic
datasets.
