Domain Adaptation for Learning Generator from Paired Few-Shot Data
This addresses the challenge of obtaining high-quality generators in data-scarce scenarios, such as in specific domains with limited paired data, though it is incremental in combining existing concepts.
The paper tackles the problem of learning generative models with limited target data by proposing a Paired Few-shot GAN (PFS-GAN) that uses domain adaptation and few-shot learning to transfer knowledge from a source domain, resulting in better quantitative and qualitative generated data with higher diversity compared to baselines.
We propose a Paired Few-shot GAN (PFS-GAN) model for learning generators with sufficient source data and a few target data. While generative model learning typically needs large-scale training data, our PFS-GAN not only uses the concept of few-shot learning but also domain shift to transfer the knowledge across domains, which alleviates the issue of obtaining low-quality generator when only trained with target domain data. The cross-domain datasets are assumed to have two properties: (1) each target-domain sample has its source-domain correspondence and (2) two domains share similar content information but different appearance. Our PFS-GAN aims to learn the disentangled representation from images, which composed of domain-invariant content features and domain-specific appearance features. Furthermore, a relation loss is introduced on the content features while shifting the appearance features to increase the structural diversity. Extensive experiments show that our method has better quantitative and qualitative results on the generated target-domain data with higher diversity in comparison to several baselines.