MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution

• URL: http://arxiv.org/abs/2606.05068v1
• Date: Wed, 03 Jun 2026 16:29:44 GMT
• Title: MaCo-GAN: Manifold-Contrastive Adversarial Learning for Single Image Super-Resolution
• Abstract summary: We propose a novel manifold-contrastive GAN framework that replaces the conventional adversarial loss with a supervised contrastive objective.<n>A core component of our method is a dynamic fake sample synthesizer that transforms ground truth (GT) data into a spectrum of challenging, perceptually plausible fake images.<n>We demonstrate consistent improvements in the perception-distortion trade-off across various benchmarks.
• Score: 5.741407725419445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 synthesizer that transforms ground truth (GT) data into a spectrum of challenging, perceptually plausible fake images that strictly maintain low-resolution (LR) correspondence. Utilizing these synthesized samples, we establish a robust contrastive minimax game: the generator is trained to attract its predictions toward on-manifold fakes (low distortion) and repel them from off-manifold fakes (high distortion), while the discriminator optimizes the exact opposite. By simply replacing the adversarial loss of a baseline SR model with our proposed objective, we demonstrate consistent improvements in the perception-distortion trade-off across various benchmarks. Extensive ablation studies validate the effectiveness of our framework and provide deep insights into the dynamics of this conditional contrastive game.

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