UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation (2203.02557v3)

Abstract: Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings between two unpaired image domains via generative-adversarial networks (GAN) coupled with the cycle-consistency constraint, while more recent works promote one-to-many mapping to boost diversity of the translated images. Motivated by scientific simulation and one-to-one needs, this work revisits the classic CycleGAN framework and boosts its performance to outperform more contemporary models without relaxing the cycle-consistency constraint. To achieve this, we equip the generator with a Vision Transformer (ViT) and employ necessary training and regularization techniques. Compared to previous best-performing models, our model performs better and retains a strong correlation between the original and translated image. An accompanying ablation study shows that both the gradient penalty and self-supervised pre-training are crucial to the improvement. To promote reproducibility and open science, the source code, hyperparameter configurations, and pre-trained model are available at https://github.com/LS4GAN/uvcgan.

• The paper proposes UVCGAN, integrating a Vision Transformer within a UNet to enforce cycle-consistency and enhance unpaired image-to-image translation.
• It employs gradient penalty techniques to stabilize GAN training, achieving lower FID and KID scores than existing state-of-the-art models.
• The study utilizes self-supervised inpainting pre-training to robustly initialize the generator for high-fidelity translations in scientific simulations.

An Evaluation of UVCGAN: A UNet Vision Transformer Cycle-Consistent GAN for Unpaired Image-to-Image Translation

In the outlined research, the authors propose UVCGAN (UNet Vision Transformer Cycle-Consistent Generative Adversarial Network), a novel approach to unpaired image-to-image translation. This model seeks to enhance the traditional CycleGAN framework by integrating a Vision Transformer (ViT) into the generator, thus maintaining cycle-consistency while improving translation performance across various domains. The paper provides comprehensive experiments on several datasets to validate the efficacy of UVCGAN compared to existing models, highlighting its potential for scientific simulations where one-to-one mappings are crucial.

Core Contributions and Methodology

The research revisits the traditional CycleGAN architecture, which is known for facilitating unpaired image translation by enforcing a cycle-consistency constraint. This constraint is essential for ensuring that the transformed images remain true to their original content, which is particularly important in scientific fields where data fidelity is paramount. However, many contemporary models have relaxed this constraint to promote diversity, sometimes at the expense of content integrity.

UVCGAN addresses this by introducing the Vision Transformer to the generator's architecture, enabling more effective learning of non-local patterns without compromising cycle-consistency. Key advancements include:

1. Hybrid Architecture: The integration of ViT within a UNet structure allows for enhanced feature extraction and modeling of spatial dependencies, which is demonstrated to outperform other architectures.
2. Training Stability: Implementing gradient penalty (GP) techniques, the authors tackle issues of instability commonly associated with GAN training. This addition is shown to stabilize convergence and improve the overall quality of the generation process.
3. Self-Supervised Pre-training: The authors employ self-supervised learning to pre-train the generator with an inpainting task, providing a robust initialization that benefits the subsequent image translation task.

Experimental Analysis

The authors validate UVCGAN's performance using datasets like Selfie2Anime, CelebA (GenderSwap and Eyeglasses tasks), demonstrating its superiority over other state-of-the-art models including ACL-GAN, Council-GAN, and U-GAT-IT. Notably, UVCGAN consistently achieves lower Fréchet Inception Distance (FID) and Kernel Inception Distance (KID) scores, indicative of higher quality and more realistic image translations.

Importantly, the model retains strong content correlation between input and translated images, a significant advantage in domains requiring precise image fidelity, such as scientific simulations. Furthermore, the successful application of self-supervised pre-training highlights the importance of leveraging unsupervised learning techniques to improve GAN training, especially in tasks requiring high-dimensional and complex data transformation.

Implications and Future Prospects

The implications of UVCGAN span both practical applications and theoretical advancements in machine learning:

• Scientific Simulations: The model holds promise for improving simulations across various scientific disciplines by reducing systematic biases between simulated and real-world data.
• AI Development: This work underscores the potential of transformers beyond natural language processing, advocating for more research into their applications in vision tasks.

Future research directions could explore further integration strategies of ViT structures in more complex architectures or the potential benefits of other transformer variants. Additionally, addressing computational efficiency during training and extending the model's applicability to larger and more diverse datasets can enhance its usability in real-world applications.

Conclusion

UVCGAN successfully combines the strengths of UNet architectures and Vision Transformers to offer a robust solution for unpaired image-to-image translation tasks. By maintaining strong cycle-consistency and leveraging advanced training techniques, UVCGAN sets a new benchmark in the field, especially for applications where content preservation is critical. This research demonstrates that well-integrated architectural changes, supported by rigorous methodological research, can yield significant advancements in generative model performance, cementing transformers as a versatile and powerful tool in the AI toolkit.