StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation (1711.09020v3)

Abstract: Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for every pair of image domains. To address this limitation, we propose StarGAN, a novel and scalable approach that can perform image-to-image translations for multiple domains using only a single model. Such a unified model architecture of StarGAN allows simultaneous training of multiple datasets with different domains within a single network. This leads to StarGAN's superior quality of translated images compared to existing models as well as the novel capability of flexibly translating an input image to any desired target domain. We empirically demonstrate the effectiveness of our approach on a facial attribute transfer and a facial expression synthesis tasks.

• The paper introduces StarGAN, a unified model that overcomes bi-domain limitations by enabling multi-domain translations with a single generator and discriminator.
• The model employs adversarial, domain classification, and reconstruction losses to ensure realistic attribute modifications while preserving image identity.
• Experiments on CelebA and RaFD datasets show StarGAN achieves lower error rates (2.12%) and superior visual quality compared to CycleGAN and DIAT.

An Assessment of StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

The paper entitled "StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation" introduces a novel approach to image-to-image translation that significantly departs from the common practice of handling bi-domain translation. The authors propose StarGAN, a single, scalable GAN-based architecture capable of translating images across multiple domains.

Motivation and Objectives

Traditional image-to-image translation models, such as CycleGAN and DIAT, are inherently limited as they are designed to address translation tasks between two specific domains. This bi-domain constraint necessitates training a separate model for each pair of domains, resulting in inefficiency and poor scalability. StarGAN aims to overcome these limitations by introducing a unified model that can perform multi-domain image-to-image translations using a single generator and discriminator. This novel approach enables concurrent training on multiple datasets with varied domain labels, thereby leveraging the full complement of training data to enhance model performance and robustness.

Methodology

StarGAN's architecture is built upon the principles of Generative Adversarial Networks (GANs), featuring a generator $G$ and a discriminator $D$. The generator is tasked with translating an input image $x$ into an output image $y$ conditioned on a target domain label $c$. This conditioning allows for flexible translation among multiple domains. The generator is accompanied by an auxiliary classifier integrated into the discriminator, facilitating classification of both real and generated images according to their domain labels.

Key components of the training paradigm include:

• Adversarial Loss: This loss encourages the generator to produce images indistinguishable from real images by the discriminator.
• Domain Classification Loss: The discriminator minimizes classification errors on real images, while the generator seeks to produce images classified correctly into the desired target domain.
• Reconstruction Loss: Inspired by cycle consistency loss, this loss ensures that the generated image retains the identity of the input image while only modifying domain-specific features.

A significant enhancement proposed in the paper is the inclusion of a mask vector, which allows StarGAN to simultaneously learn from multiple datasets with incomplete label information. This facilitates broad applicability and enhances the generalization capability of the network by maximizing the use of all available training data.

Results

The authors empirically validate StarGAN's efficacy through extensive experiments on the CelebA and RaFD datasets. The following findings were observed:

• Qualitative Assessment: Visual comparisons indicate that StarGAN produces high-quality image translations with well-preserved facial identity features and realistic attribute modifications. Unlike DIAT and CycleGAN, StarGAN manages to avoid blurriness and artifacts, demonstrating superior visual fidelity.
• User Studies: Mechanical Turk user studies highlighted StarGAN's dominance in both single-attribute and multi-attribute facial transformations. The model received the highest votes in perceptual realism, attribute translation accuracy, and identity preservation.
• Quantitative Metrics: Classification errors for synthesized facial expressions were lowest with StarGAN (2.12%), outperforming DIAT and CycleGAN by significant margins, demonstrating the model's high accuracy in generating target-domain-specific artistic renditions. Additionally, StarGAN's scalability is evident as it uses substantially fewer parameters than the baseline models.

Implications and Future Directions

The implications of StarGAN extend beyond mere improvements in computational efficiency and scalability:

1. Theoretical Advancements: StarGAN introduces a methodologically sound mechanism for multi-domain translation within a unified architecture. The joint training approach with a mask vector offers key insights into overcoming the challenge of partial labels in multi-dataset scenarios.
2. Practical Applications: From a practical lens, this method facilitates a wide array of image-editing applications including but not limited to facial attribute transfer, expression synthesis, and potentially style transfer.
3. Future Prospects in AI: The introduction of StarGAN sets a precedent for further exploration into unified architectures capable of handling more complex and diverse translation tasks. Future research could delve into extending this model for high-resolution imagery, dynamically conditioned GANs, and application-specific enhancements.

In summary, StarGAN represents a significant contribution to the domain of image-to-image translation through its scalable, unified approach capable of handling multi-domain translations effectively. This paper provides a strong fundament for future explorations and enhancements in generating high-quality, realistic images across diverse domains leveraging generative adversarial networks.