Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations
Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations
Training deep generative models usually requires a large amount of data. To alleviate the data collection cost, the task of zero-shot GAN adaptation aims to reuse well-trained generators to synthesize images of an unseen target domain without any further training samples. Due to the data absence, the textual description of …