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.
Speakers: Miaoyun Zhao, Yulai Cong, Lawrence Carin