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The image shown is from the anime character faces generation project in the paper “Towards the Automatic Anime Characters Creation with Generative Adversarial Networks.” The image is generated with the fixed noise part and random attributes. For more information, check out the paper.
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This GANs image is from a famous paper, “A Style-Based Generator Architecture for Generative Adversarial Networks,” which is associated with the generation of the Flicker Faces HQ dataset. The paper studies an alternative generator architecture for GAN with some style transfer literature. The image presented here includes the images synthesized by mixing two latent codes at various scales.
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The text-2-image synthesis is a topic of interest for many researchers. Here the image shown is the GANs image from the paper “Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks,” which is a result of GAN trained with cycle loss and frozen weight of image captioning network.
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As mentioned above, the paper “StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks” provides a significant output in text-2-image synthesis. The GANs image here is the image generated by StackGAN, which is very photo-realistic.
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As mentioned in the image-to-image translation, the paper “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” has one of the best GANs images generated to date. Here, the above image is the result of CycleGAN on paired datasets used in pix2pix.
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The image shown is from the paper “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks” and is the collection style transfer II, showcasing the successful style transfer into the artistic styles of Monet, Van Gogh, Cezanne, and Ukiyo-e.
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This is a GANs image from the paper “P²-GAN: EFFICIENT STYLE TRANSFER USING SINGLE STYLE IMAGE.” It shows a comparative image of the style transfer from a single style image using various methods, including JohnsonNet, TextureNetIN, MGAN, and P²-GAN.