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What is Dual Contrastive Learning For Unsupervised Image-to-Image Translation about?

The paper presents a novel approach called Dual Contrastive Learning (DCLGAN) for unsupervised image-to-image translation, which utilizes two encoders to improve mapping efficiency between unpaired data. DCLGAN addresses limitations of existing methods like CycleGAN and CUT by maximizing mutual information and mitigating mode collapse, thus enhancing image diversity and translation quality. Extensive experiments demonstrate its superior performance over state-of-the-art methods across various challenging ta

Author

luffyxnami8a

Language

EN