Few Shot Generative Domain Adaptation Via Inference-Stage Latent Learning in GANs

In this study, we adapt generative models trained on large source datasets to scarce target domains. We adapt a pre-trained Generative Adversarial Network (GAN) without retraining the generator, avoiding catastrophic forgetting and over-fitting. Starting from the observation that target images can be `embedded' onto the latent space of a pre-trained source-GAN, our method finds the latent code corresponding to the target domain on the source latent manifold. Optimizing a latent learner network during inference generates a novel target embedding that is supplied to the source-GAN generator to generate target samples. Our method, albeit simple, can be used to generate data from multiple target distributions using a generator trained on a single source distribution.