CoFi-UCGen achieves both coarse- and fine-grained unsupervised conditional image generation by using bit-codes for structured latent space and hierarchical modulation in diffusion models.
Training gans with stronger augmen- tations via contrastive discriminator,
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
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.
citing papers explorer
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CoFi-UCGen: Coarse-to-Fine Unsupervised Conditional Generation without Label Priors
CoFi-UCGen achieves both coarse- and fine-grained unsupervised conditional image generation by using bit-codes for structured latent space and hierarchical modulation in diffusion models.
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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.