iFlowGAN : An Invertible Flow-Based Generative Adversarial Network for Unsupervised Image-to-Image Translation
We propose iFlowGAN that learns an invertible flow (a sequence of invertible mappings) via adversarial learning and exploit it to transform a source distribution into a target distribution for unsupervised image-to-image translation. Existing GAN-based generative model such as CycleGAN [1], StarGAN...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 44(2022), 8 vom: 15. Aug., Seite 4151-4162 |
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| Format: | Article en ligne |
| Langue: | English |
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2022
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article |
| Accès en ligne |
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