RelGAN conditions a GAN on relative attribute vectors, the difference between target and source attributes, to achieve continuous facial attribute editing with better preservation of unchanged attributes than StarGAN and AttGAN.
KG-GAN: Knowledge-Guided Generative Adversarial Networks
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abstract
Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input? The answer is negative, since GANs' discriminator would reject all roses of unseen petal colors. In this study, we propose knowledge-guided GAN (KG-GAN) to fuse domain knowledge with the GAN framework. KG-GAN trains two generators; one learns from data whereas the other learns from knowledge with a constraint function. Experimental results demonstrate the effectiveness of KG-GAN in generating unseen flower categories from seen categories given textual descriptions of the unseen ones.
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cs.CV 1years
2019 1verdicts
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RelGAN: Multi-Domain Image-to-Image Translation via Relative Attributes
RelGAN conditions a GAN on relative attribute vectors, the difference between target and source attributes, to achieve continuous facial attribute editing with better preservation of unchanged attributes than StarGAN and AttGAN.