Learning Latent Representations for Image Translation using Frequency Distributed CycleGAN

• URL: http://arxiv.org/abs/2508.03415v1
• Date: Tue, 05 Aug 2025 12:59:37 GMT
• Title: Learning Latent Representations for Image Translation using Frequency Distributed CycleGAN
• Authors: Shivangi Nigam, Adarsh Prasad Behera, Shekhar Verma, P. Nagabhushan,
• Abstract summary: Fd-CycleGAN is an image-to-image (I2I) translation framework that enhances latent representation learning to approximate real data distributions.<n>We conduct experiments on diverse datasets -- Horse2Zebra, Monet2Photo, and a synthetically augmented Strike-off dataset.<n>Our results suggest that frequency-guided latent learning significantly improves generalization in image translation tasks.
• Score: 7.610968152027164
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents Fd-CycleGAN, an image-to-image (I2I) translation framework that enhances latent representation learning to approximate real data distributions. Building upon the foundation of CycleGAN, our approach integrates Local Neighborhood Encoding (LNE) and frequency-aware supervision to capture fine-grained local pixel semantics while preserving structural coherence from the source domain. We employ distribution-based loss metrics, including KL/JS divergence and log-based similarity measures, to explicitly quantify the alignment between real and generated image distributions in both spatial and frequency domains. To validate the efficacy of Fd-CycleGAN, we conduct experiments on diverse datasets -- Horse2Zebra, Monet2Photo, and a synthetically augmented Strike-off dataset. Compared to baseline CycleGAN and other state-of-the-art methods, our approach demonstrates superior perceptual quality, faster convergence, and improved mode diversity, particularly in low-data regimes. By effectively capturing local and global distribution characteristics, Fd-CycleGAN achieves more visually coherent and semantically consistent translations. Our results suggest that frequency-guided latent learning significantly improves generalization in image translation tasks, with promising applications in document restoration, artistic style transfer, and medical image synthesis. We also provide comparative insights with diffusion-based generative models, highlighting the advantages of our lightweight adversarial approach in terms of training efficiency and qualitative output.

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