Data InStance Prior (DISP) in Generative Adversarial Networks

Understand

Recent advances in generative adversarial networks (GANs) have shown remarkable progress in generating high-quality images.

• However, this gain in performance depends on the availability of a large amount of training data.
• In limited data regimes, training typically diverges, and therefore the generated samples are of low quality and lack diversity.
• Previous works have addressed training in low data setting by leveraging transfer learning and data augmentation techniques.