Comparing Implicit and Denoising Score-Matching Objectives

Paper Abstract Score estimation has led to several state-of-the-art generative models, particularly in computer vision. Compared to maximum likelihood, one of the key advantages of score estimation is that it does not require the calculation of a normalization factor. However, explicit score matching necessitates knowledge of the true score of the data distribution, which is typically unavailable.…