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One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU
We propose a one-shot ultra-high-resolution generative adversarial network (OUR-GAN) framework that generates non-repetitive 16K (16, 384 x 8, 640) images from a single training image and is trainable on a single consumer GPU.
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