Recent work has shown GANs can generate highly realistic images that are indistinguishable by human. Of particular interest here is the empirical observation that most generated images are not contained in training datasets, indicating potential generalization with GANs. That generalizability makes it appealing to exploit GANs to help applications with limited available data, e.g., augment training data to alleviate overfitting. To better facilitate training a GAN on limited data, we propose to leverage already-available GAN models pretrained on large-scale datasets (like ImageNet) to introduce additional common knowledge (which may not exist within the limited data) following the transfer learning idea. Specifically, exampled by natural image generation tasks, we reveal the fact that low-level filters (those close to observations) of both the generator and discriminator of pretrained GANs can be transferred to help the target limited-data generation. For better adaption of the transferred filters to the target domain, we introduce a new technique named adaptive filter modulation (AdaFM), which provides boosted performance over baseline methods. Unifying the transferred filters and the introduced techniques, we present our method and conduct extensive experiments to demonstrate its training efficiency and better performance on limited-data generation.

本研究利用预训练的大规模GAN模型在有限数据情境下实现了对感知明显目标领域的图像生成，提出了自适应滤波器调制（AdaFM）的方法，证明了此技术在限制数据生成方面的有效性。

利用预训练的 GAN 生成有限数据