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Yang Song | Generative Modeling by Estimating Gradients of the Data Distribution
yang-song.github.io · 6,258 words · saved by 1 readers
Yang Song's academic website.
Generative Modeling by Estimating Gradients of the Data Distribution | Yang Song Generative Modeling by Estimating Gradients of the Data Distribution This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models , has several important advantages over existing model families: GAN-level sample quality without adversarial t
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