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Improved Input Reprogramming for GAN Conditioning. (arXiv:2201.02692v2 [cs.LG] UPDATED)
Jan. 13, 2022, 2:10 a.m. | Tuan Dinh, Daewon Seo, Zhixu Du, Liang Shang, Kangwook Lee
cs.LG updates on arXiv.org arxiv.org
We study the GAN conditioning problem, whose goal is to convert a pretrained
unconditional GAN into a conditional GAN using labeled data. We first identify
and analyze three approaches to this problem -- conditional GAN training from
scratch, fine-tuning, and input reprogramming. Our analysis reveals that when
the amount of labeled data is small, input reprogramming performs the best.
Motivated by real-world scenarios with scarce labeled data, we focus on the
input reprogramming approach and carefully analyze the existing algorithm. …
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