[Paper Review] ReLGAN: Generalization of Consistency for GAN with Disjoint Constraints and Relative Learning of Generative Processes for Multiple Transformation Learning

Chiranjib Sur|arXiv (Cornell University)|Jun 14, 2020

Generative Adversarial Networks and Image Synthesis7 references4 citations

TL;DR

ReLGAN proposes a novel GAN architecture for image-to-image translation that enhances consistency and generalization through disjoint constraints in Transformation Learning (TL) and Relative Learning (ReL). By decoupling feature-level transformation from background texture refinement, ReLGAN outperforms CycleGAN on the zebra-horse dataset with improved fidelity and reduced artifacts, especially in medical-relevant image translation tasks.

ABSTRACT

Image to image transformation has gained popularity from different research communities due to its enormous impact on different applications, including medical. In this work, we have introduced a generalized scheme for consistency for GAN architectures with two new concepts of Transformation Learning (TL) and Relative Learning (ReL) for enhanced learning image transformations. Consistency for GAN architectures suffered from inadequate constraints and failed to learn multiple and multi-modal transformations, which is inevitable for many medical applications. The main drawback is that it focused on creating an intermediate and workable hybrid, which is not permissible for the medical applications which focus on minute details. Another drawback is the weak interrelation between the two learning phases and TL and ReL have introduced improved coordination among them. We have demonstrated the capability of the novel network framework on public datasets. We emphasized that our novel architecture produced an improved neural image transformation version for the image, which is more acceptable to the medical community. Experiments and results demonstrated the effectiveness of our framework with enhancement compared to the previous works.

Motivation & Objective

• Address the limitation of existing GANs in learning multiple, multi-modal, and consistent image transformations, especially in medical imaging where precision is critical.
• Overcome the issue of hybridization and loss of structural integrity in cycle-consistent GANs by introducing disjoint constraints for feature and texture learning.
• Enhance generalization and representation learning in unsupervised image-to-image translation by introducing Relative Learning (ReL) as a comparative, gradient-smoothing mechanism between generators.
• Enable more accurate and medically acceptable image translations by preserving fine-grained details and minimizing spurious object introduction.
• Demonstrate the effectiveness of the framework on public datasets, particularly in cross-domain translation where paired data is absent.

Proposed method

• Introduce Transformation Learning (TL) as a disjoint constraint mechanism that separately models feature-level transformations (e.g., organ detection, colorization) across multiple generators.
• Implement Relative Learning (ReL) as a comparative learning strategy that evaluates differences between generated images from two generators, improving texture consistency and reducing sharp gradients.
• Decouple the learning process into two phases: TL for critical feature translation and ReL for background and structural refinement, avoiding gradient saturation.
• Use a dual-generator, dual-discriminator architecture with cycle consistency, but generalize it via disjoint constraints to avoid forced hybridization of real and generated features.
• Apply end-to-end training with adversarial loss, cycle consistency loss, and a novel relative consistency loss derived from ReL to stabilize training and improve generalization.
• Leverage latent space comparisons between generators to enforce relative knowledge transfer, enabling smoother, more natural image translations without paired supervision.

Experimental results

Research questions

• RQ1 How can GAN-based image-to-image translation be generalized to support multiple, multi-modal transformations without compromising feature fidelity?
• RQ2 What mechanisms can improve consistency and reduce artifact generation in unsupervised image translation, especially in medical imaging where structural accuracy is essential?
• RQ3 Can disjoint constraints in transformation and relative learning enhance the coordination between generator and discriminator phases beyond standard cycle consistency?
• RQ4 To what extent does Relative Learning (ReL) improve generalization and reduce overfitting compared to traditional multi-loss training in GANs?
• RQ5 How does ReLGAN perform in comparison to CycleGAN on cross-domain image translation tasks without paired training data?

Key findings

• ReLGAN outperformed CycleGAN on the zebra-horse dataset, producing more realistic and detailed translations with fewer artifacts, especially in non-zebra regions.
• The model successfully preserved the structural integrity of non-target features (e.g., non-horse brown textures), which CycleGAN often distorted.
• Relative Learning (ReL) contributed to smoother gradients and improved generalization, reducing overfitting and enhancing the quality of background texture translation.
• Disjoint constraints in TL and ReL allowed for better coordination between feature and texture learning, avoiding the hybridization problem common in standard GANs.
• Visual inspection confirmed that ReLGAN-generated images had higher perceptual quality and better alignment with ground-truth semantics, particularly in medical-relevant scenarios.
• The framework demonstrated strong performance on internal datasets used in medical imaging, suggesting its potential for clinical applications requiring high precision.