What denoising score matching does: learns to denoise, which equals learning the score

What score matching does: learns the gradient of the log-density without normalizing

Score matching approximates log-density gradients for variational inference without normalization

How does score matching utilize the Fisher Information Matrix to learn the parameters of a probabilistic model without normalizing the score?

Score matching estimates parameters by minimizing the Kullback-Leibler divergence between empirical and model score distributions

What DDPM stands for: Denoising Diffusion Probabilistic Model

DDPM: Denoising Diffusion Probabilistic Model for generative tasks

What AdaGrad does: divides learning rate by sqrt of sum of squared gradients

AdaGrad adapts learning rates based on historical gradients, reducing for frequently updated features

What IS (Inception Score) measures: diversity and quality of generated images

Inception Score quantifies image diversity and generated images' quality

What cutmix does: replaces a patch of one image with a patch from another

Patch-based image cutmix swaps image sections for data augmentation