Learning Latent Representations for Image Translation using Frequency Distributed CycleGAN

TL;DR

This paper introduces Fd-CycleGAN, an advanced image translation framework that leverages frequency-aware supervision and local neighborhood encoding to improve the quality, diversity, and efficiency of image-to-image translation tasks.

Contribution

It proposes a novel frequency-guided latent learning approach integrated into CycleGAN, enhancing distribution alignment and translation quality in low-data regimes.

Findings

Superior perceptual quality over baseline methods

Faster convergence and improved mode diversity

Effective in low-data and diverse dataset scenarios

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.