Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models

Understand

The proposed method, Discriminator Guidance, aims to improve sample generation of pre-trained diffusion models.

• The approach introduces a discriminator that gives explicit supervision to a denoising sample path whether it is realistic or not.
• Unlike GANs, our approach does not require joint training of score and discriminator networks.
• Instead, we train the discriminator after score training, making discriminator training stable and fast to converge.