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Most improvements from recent GANs' papers usually tinker with the network architecture or loss functions used. Eg. recent near-human quality StyleGANv2 used progressive growing networks, or the GAN that coined image-to-image translation used modified loss function to better learn mapping between images. You can try explore those two factors
Most improvements from recent GANs' papers usually tinker with the network architecture or loss functions used. Eg. recent near-human quality StyleGANv2 used progressive growing networks, or the GAN that coined image-to-image translation used modified loss function to better learn mapping between images. You can try explore those two factors
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