rejection sampling does: samples from target by accepting/rejecting proposals

importance sampling does: reweights samples from proposal to estimate target expectation

Importance sampling reweights samples from a proposal distribution to estimate the expectation under a target distribution

Resampling (statistics)

Bootstrapping samples with replacement to estimate distributions

Metropolis–Hastings algorithm

Metropolis-Hastings algorithm samples from difficult distributions

DDIM does: deterministic sampling for faster generation with fewer steps

DDIM accelerates image generation by deterministically sampling intermediate steps

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

Denoising score matching learns to denoise by estimating the score (gradient of log probability) of data distributions

classifier-free guidance does: interpolates between conditional and unconditional generation

"Classifies samples as either conditioned or unconditioned, guiding generation towards desired outcomes."