On Leveraging Pretrained GANs for Limited-Data Generation
Year: 2020
Type: preprint
Abstract: Recent work has shown GANs can generate highly realistic images that areindistinguishable 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.
Source: arXiv (Cornell University)
Authors: Miaoyun Zhao, Yulai Cong, Lawrence Carin
Institution: Duke University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3007166982