Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional Spaces

13 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling (2020, methodology): “This has been applied to deep EBMs (Saremi et al., 2018) and recently scaled to high resolution image data (Song & Ermon, 2019; Li et al., 2019) with compelling results.”
Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser (2021, unknown): “How can we make use of this implicit prior? Recent results have derived relationships between Score Matching density estimates and denoising, and have used these relationships to make use of implicit prior information [43, 44, 29, 45].”
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models (2020, unknown): “…minimizes the expected squared distance between the score function of the data distribution and that of the model distribution, and its variants (Kingma & LeCun, 2010; Vincent, 2011; Saremi et al., 2018; Song et al., 2019; Li et al., 2019; Pang et al., 2020) have shown promise in learning EBMs.”
Efficient Learning of Generative Models via Finite-Difference Score Matching (2020, methodology): “6 Experiments In this section, we experiment on a diverse set of generative models, following the default settings in previous work [36, 57, 58].”