Emergent Mind

DualGAN: Unsupervised Dual Learning for Image-to-Image Translation

(1704.02510)

Published Apr 8, 2017 in cs.CV

Abstract

Conditional Generative Adversarial Networks (GANs) for cross-domain image-to-image translation have made much progress recently. Depending on the task complexity, thousands to millions of labeled image pairs are needed to train a conditional GAN. However, human labeling is expensive, even impractical, and large quantities of data may not always be available. Inspired by dual learning from natural language translation, we develop a novel dual-GAN mechanism, which enables image translators to be trained from two sets of unlabeled images from two domains. In our architecture, the primal GAN learns to translate images from domain U to those in domain V, while the dual GAN learns to invert the task. The closed loop made by the primal and dual tasks allows images from either domain to be translated and then reconstructed. Hence a loss function that accounts for the reconstruction error of images can be used to train the translators. Experiments on multiple image translation tasks with unlabeled data show considerable performance gain of DualGAN over a single GAN. For some tasks, DualGAN can even achieve comparable or slightly better results than conditional GAN trained on fully labeled data.

Overview

DualGAN introduces an unsupervised dual learning framework for image-to-image translation, leveraging unlabeled images from different domains.
The architecture employs a primal and dual GAN setup to enable bidirectional image translation, enhancing translation quality through a loopback reconstruction loss.
Empirical results showcase DualGAN's superiority over traditional and supervised counterparts in texture and style translation tasks.
It opens opportunities for applications in fields where paired datasets are scarce, despite facing challenges with highly disparate domains.

Unveiling DualGAN: An Unsupervised Approach for Dual Learning in Image-to-Image Translation

Overview

Dual learning mechanisms have been established within the realm of NLP, yet their application in the area of image-to-image translation has remained largely unexplored until the introduction of DualGAN. This novel architecture harnesses the potential of unsupervised learning, leveraging two sets of unlabeled images from distinct domains to train image translators. By orchestrating a primal and dual GAN (Generative Adversarial Network), DualGAN enables the translation of images from one domain to another and vice versa, facilitating a closed-loop that significantly augments the efficacy of the translation process, sans the reliance on labeled data.

Architectural Insights

DualGAN innovates by employing a dual learning scheme inspired from NLP, adjusting it to suit the challenges inherent in image translation tasks. The mechanism channels the power of two GANs, each dedicated to undertaking the primal and dual tasks of image translation between two domains. This setup allows for a learning process that heavily relies on unlabeled image data, a significant leap from previous methodologies which necessitated labeled pairs, thereby limiting scalability and applicability.

Unsupervised Learning Dynamics

The backbone of DualGAN's success lies in its symbiotic primal-dual learning procedure, using only unlabeled data.

The primal GAN focuses on translating images from domain A to B, while the dual GAN takes on the inverse.
A key innovation here is the inclusion of reconstruction loss in the training regimen, significantly enhancing the quality of translations by ensuring that an image translated to a second domain can be translated back to its original form with minimal loss.

Network Configuration

The implementation details reveal a thoughtful design tailored to accommodate the nuances of image data.

Both generators (primal and dual) employ Fully Convolutional Networks (FCNs) characterized by a series of downsampling and upsampling layers interconnected by skip connections, elegantly preserving low-level information across the translation process.
Discriminators in the architecture utilize Markovian PatchGANs, adept at distinguishing between real and "fake" images at a patch level, hence emphasizing local texture and style fidelity over global coherence.

Empirical Validation

The empiric results presented in the paper delineate significant achievements.

When pitted against traditional GAN frameworks and supervised cGAN systems, DualGAN displays superior performance in tasks relying on texture and stylistic translations, even in the absence of paired training data.
For instance, in the conversion of day scenes to night scenes, DualGAN yields visually convincing translations that well-capture the essence of the target domain, outperforming its counterparts in several benchmarks.

Theoretical and Practical Implications

The inception of DualGAN marks a promising advance in the field of image-to-image translation, particularly within the unsupervised learning spectrum.

Theoretically, it demonstrates the feasibility and efficacy of dual learning paradigms in domains beyond NLP, endorsing the application of such models in broader AI disciplines.
Practically, the ability to harness unlabeled data opens vast avenues for applications in areas where paired datasets are scarce or...challenging to procure, significantly broadening the horizon of possible applications and experiments in image translation.

Looking Forward

Despite its notable achievements, the architecture exhibits limitations in translating between highly dissonant domains, such as those involving semantics-based labels. Future explorations may delve into hybrid models that integrate minimal supervision to overcome these challenges, further expanding the utility and effectiveness of DualGAN.

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