InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs
IEEE Transactions on Pattern Analysis and Machine Intelligence(2022)
摘要
Although generative adversarial networks (GANs) have made significant progress in face synthesis, there lacks enough understanding of what GANs have learned in the latent representation to map a random code to a photo-realistic image. In this work, we propose a framework called InterFaceGAN to interpret the disentangled face representation learned by the state-of-the-art GAN models and study the p...
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关键词
Semantics,Faces,Gallium nitride,Generative adversarial networks,Generators,Aerospace electronics,Facial features
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