Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution
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MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution
new Abstract: Conventional Generative Adversarial Networks (GANs) for Single Image Super-Resolution (SISR) often struggle with hallucinated artifacts, largely because standard discriminators evaluate overall image naturalness rather than strict conditional realism. To address this, we propose MaCo-GAN, a novel manifold-contrastive GAN framework that replaces the conventional adversarial loss with a supervised contrastive objective. A core component of our method is a dynamic fake sample...