Coupled Generative Adversarial Networks (1606.07536v2)

Abstract: We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images. It can learn a joint distribution with just samples drawn from the marginal distributions. This is achieved by enforcing a weight-sharing constraint that limits the network capacity and favors a joint distribution solution over a product of marginal distributions one. We apply CoGAN to several joint distribution learning tasks, including learning a joint distribution of color and depth images, and learning a joint distribution of face images with different attributes. For each task it successfully learns the joint distribution without any tuple of corresponding images. We also demonstrate its applications to domain adaptation and image transformation.

• The paper introduces a novel weight-sharing approach that allows learning joint distributions across image domains without paired training data.
• It employs dual generators and discriminators with shared layers to capture common high-level semantics while preserving domain-specific details.
• Experimental results show high pixel agreement ratios and a 72% error reduction in digit classification, underscoring its effectiveness in domain adaptation.

Coupled Generative Adversarial Networks: An Overview

Introduction

The paper "Coupled Generative Adversarial Networks" by Ming-Yu Liu and Oncel Tuzel presents a novel framework, CoGAN, for learning joint distributions of multi-domain images. The fundamental contribution of this work lies in its methodology, which can learn joint distributions without requiring tuples of corresponding images in different domains during training. This approach is a significant divergence from traditional methods, which necessitate paired data.

Core Methodology

CoGAN consists of a pair of generative adversarial networks (GANs), each responsible for synthesizing images in one domain. The framework leverages a weight-sharing mechanism across generative and discriminative models in the GANs. This weight-sharing is crucial as it forces the models to learn a shared representation for high-level semantics, promoting the learning of a joint distribution over a mere product of marginal distributions. The process breaks down as follows:

1. Generative Models: Each GAN in the pair generates images for one domain. The shared weights in the initial layers of the generative models ensure that the high-level semantics are consistently decoded across both domains.
2. Discriminative Models: Each GAN also includes a discriminative model that distinguishes real images from generated ones. To align the high-level features extracted from both domains, the weights of the final layers of these discriminative models are shared.

Numerical Results and Analysis

The paper provides empirical evidence demonstrating the efficacy of CoGAN on various tasks, such as generating corresponding images in digit and color-defined domains. For instance, in experiments involving the MNIST dataset:

• Task A: Learning a joint distribution of digits and their corresponding edge images resulted in an average pixel agreement ratio of 0.952.
• Task B: Learning a joint distribution of digits and their negative images achieved an average pixel agreement ratio of 0.967.

Further experiments with facial images (using the CelebFaces Attributes dataset) and RGB-D images (using the RGBD and NYU datasets) also showed successful generation of paired images, reinforcing the versatility of CoGAN.

Comparative Analysis

A critical comparison was made between CoGAN and a conditional GAN for joint image distribution tasks. The CoGAN outperformed the conditional GAN significantly in tasks A and B, underscoring the importance of the weight-sharing mechanism. Specifically, CoGAN achieved a 72% error reduction rate in a digit classification domain adaptation task compared to the state-of-the-art at that time.

Theoretical Implications

The theoretical underpinnings of this work suggest that adversarial training alone isn't sufficient for learning joint distributions without corresponding data. The weight-sharing constraint plays a pivotal role in enabling the models to realize shared high-level abstractions, which then adapt to domain-specific details through respective unrestricted final layers.

Practical Implications

The practical implications of CoGAN are far-reaching. Applications include:

• Unsupervised Domain Adaptation: Adapting classifiers across domains without labeled data in the target domain.
• Cross-Domain Image Transformation: Generating corresponding images across different styles or modalities, which is essential in tasks like image colorization, style transfer, and medical imaging.

Future Directions

This research opens multiple avenues for future investigation. Enhancing the weight-sharing mechanism to capture more complex and abstract relationships between domains could broaden the applicability of CoGAN. Moreover, integrating CoGAN with other neural network architectures, such as RNNs for sequential data, presents another promising direction. Exploring larger and more diverse datasets will further validate and refine the framework.

Conclusion

The "Coupled Generative Adversarial Networks" paper introduces a robust framework for unsupervised learning of joint distributions across multiple image domains. By innovatively employing weight-sharing constraints, the CoGAN model achieves remarkable results in generating corresponding multi-domain images without the need for paired training data. This work not only opens up new possibilities for generative models but also significantly impacts areas like domain adaptation and image transformation.