Unlocking new dimensions in image-generation research with Manifold Matching via Metric Learning
Generative image models offer a unique value by creating new images. Such images can be sharp super-resolution versions of existing images or even realistic-looking synthetic photographs. Generative Adversarial Networks (GANs) and their variants have demonstrated pioneering success with the framework of training two networks against each other: a generator network learns to generate realistic fake data that can trick a discriminator network, and the discriminator network learns to correctly tell apart the generated fake data from the real data.
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