Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation. (arXiv:2210.10715v1 [cs.LG])

We introduce a simple modification to the standard maximum likelihood
estimation (MLE) framework. Rather than maximizing a single unconditional
likelihood of the data under the model, we maximize a family of \textit{noise
conditional} likelihoods consisting of the data perturbed by a continuum of
noise levels. We find that models trained this way are more robust to noise,
obtain higher test likelihoods, and generate higher quality images. They can
also be sampled from via a novel score-based sampling scheme which combats …

arxiv likelihood maximum likelihood estimation modeling noise