MaCo-GAN introduces a manifold-contrastive GAN that replaces adversarial loss with a contrastive minimax game over synthesized fake samples to improve the perception-distortion trade-off in SISR.
In: Proceedings of the IEEE conference on computer vision and pattern recognition
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MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution
MaCo-GAN introduces a manifold-contrastive GAN that replaces adversarial loss with a contrastive minimax game over synthesized fake samples to improve the perception-distortion trade-off in SISR.