Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping (1809.05852v2)

Abstract: Unsupervised domain mapping aims to learn a function to translate domain X to Y by a function GXY in the absence of paired examples. Finding the optimal GXY without paired data is an ill-posed problem, so appropriate constraints are required to obtain reasonable solutions. One of the most prominent constraints is cycle consistency, which enforces the translated image by GXY to be translated back to the input image by an inverse mapping GYX. While cycle consistency requires the simultaneous training of GXY and GY X, recent studies have shown that one-sided domain mapping can be achieved by preserving pairwise distances between images. Although cycle consistency and distance preservation successfully constrain the solution space, they overlook the special properties that simple geometric transformations do not change the semantic structure of images. Based on this special property, we develop a geometry-consistent generative adversarial network (GcGAN), which enables one-sided unsupervised domain mapping. GcGAN takes the original image and its counterpart image transformed by a predefined geometric transformation as inputs and generates two images in the new domain coupled with the corresponding geometry-consistency constraint. The geometry-consistency constraint reduces the space of possible solutions while keep the correct solutions in the search space. Quantitative and qualitative comparisons with the baseline (GAN alone) and the state-of-the-art methods including CycleGAN and DistanceGAN demonstrate the effectiveness of our method.

• The paper introduces a geometry-consistency constraint in GANs to address one-sided unsupervised domain mapping without paired data.
• It leverages geometric transformations like rotations to regularize image translation and combat mode collapse.
• Experimental results on datasets such as SVHN→MNIST demonstrate competitive performance versus CycleGAN and DistanceGAN.

Overview of Geometry-Consistent Generative Adversarial Networks for One-Sided Unsupervised Domain Mapping

The paper under review introduces Geometry-Consistent Generative Adversarial Networks (GcGAN), advancing the field of unsupervised domain mapping. This paper presents a novel approach by integrating a geometry-consistency constraint into generative adversarial networks (GANs), offering a solution to the one-sided unsupervised domain mapping problem. This research is pivotal as it achieves domain translation without the simultaneous training of inverse mappings, distinguishing it from the traditional cycle-consistency approaches.

Background and Motivation

In domain mapping, the task is to translate images from one domain, $\mathcal{X}$, to another, $\mathcal{Y}$, without relying on paired examples. The cycle-consistency constraint, popularized by CycleGAN, typically enforces both the forward and backward mappings for this task, assuming that the translated image can be reverted back to its original form. Another method, DistanceGAN, addresses one-sided mapping by preserving pairwise distances between images. However, these methods largely overlook the geometric properties inherent to images, which remain unchanged by simple transformations such as rotations and flips.

Methodology

The core innovation of GcGAN is its geometry-consistency constraint. This constraint leverages the fact that geometric transformations, like rotations, do not alter an image's semantic structure. By using these transformations, GcGAN reduces the space of possible solutions, ensuring that geometry-consistent image translations are maintained. In practice, GcGAN takes both an original image and its geometrically transformed counterparts as inputs to generate corresponding images in the target domain. This approach helps to regularize the generated images, mitigating mode collapse and ensuring sensible domain mapping.

Experimentation and Results

The authors conducted both quantitative and qualitative comparisons against established methods like CycleGAN and DistanceGAN, demonstrating the effectiveness of GcGAN. Experiments on various datasets, including Cityscapes, Google Maps, and SVHN$\to$MNIST, confirm that GcGAN achieves competitive or superior performance relative to state-of-the-art methods. For instance, GcGAN outperformed DistanceGAN and CycleGAN significantly in the SVHN$\to$MNIST task, indicating its robust capability in learning meaningful mappings without paired data.

Moreover, ablation studies reveal the robustness of the geometry-consistency constraint, showing that it can operate independently of or complementary to other constraints, such as cycle-consistency and distance preservation. This adaptability enhances GcGAN's practical applications across different kinds of domain translation tasks.

Implications and Future Directions

The primary implication of this research lies in its ability to perform unsupervised domain mapping effectively without requiring paired examples or simultaneous inverse mappings. This makes GcGAN particularly relevant in scenarios where obtaining paired data is expensive or impractical, such as in artistic style transfer or synthetic-to-real image translation tasks.

Future research could explore the incorporation of more complex geometric transformations into the framework or the integration of complementary unsupervised constraints to further elevate performance. Additionally, there is potential in extending this model to multi-domain settings or enhancing its robustness against more challenging image variations and transformations.

In conclusion, this work offers a significant advancement in the domain mapping field, presenting a flexible and efficient alternative to existing adversarial methods. GcGAN not only broadens the methodological toolkit available for this type of image translation but also sets a foundational basis for further innovations in geometry-consistent models for AI applications.