Few Shot Generative Model Adaption via Relaxed Spatial Structural Alignment

Understand

Training a generative adversarial network (GAN) with limited data has been a challenging task.

• A feasible solution is to start with a GAN well-trained on a large scale source domain and adapt it to the target domain with a few samples, termed as few shot generative model adaption.
• However, existing methods are prone to model overfitting and collapse in extremely few shot setting (less than 10).
• To solve this problem, we propose a relaxed spatial structural alignment method to calibrate the target generative models during the adaption.