Dual Generator Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
State-of-the-art methods for image-to-image translation with Generative Adversarial Networks (GANs) can learn a mapping from one domain to another domain using unpaired image data. However, these methods require the training of one specific model for every pair of image domains, which limits the scalability in dealing with more than two image domains. In addition, the training stage of these methods has the common problem of model collapse that degrades the quality of the generated images. To tackle these issues, we propose a Dual Generator Generative Adversarial Network (G$^2$GAN), which is a robust and scalable approach allowing to perform unpaired image-to-image translation for multiple domains using only dual generators within a single model. Moreover, we explore different optimization losses for better training of G$^2$GAN, and thus make unpaired image-to-image translation with higher consistency and better stability. Extensive experiments on six publicly available datasets with different scenarios, i.e., architectural buildings, seasons, landscape and human faces, demonstrate that the proposed G$^2$GAN achieves superior model capacity and better generation performance comparing with existing image-to-image translation GAN models.
Code (1)
Tasks
Generative Adversarial NetworkImage-to-Image TranslationTranslationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution …
Generative Adversarial NetworkDual-Domain Equivariant Generative Adversarial Network for Multimodal CT-PET Synthesis
We present a Dual-Domain Equivariant Generative Adversarial Network (DDE-GAN) for multimodal CT-PET image synthesis. Traditional GAN-based approaches often operate solely in the spatial domain and ignore geometric consis…
Data AugmentationCDE-GAN: Cooperative Dual Evolution Based Generative Adversarial Network
Generative adversarial networks (GANs) have been a popular deep generative model for real-world applications. Despite many recent efforts on GANs that have been contributed, mode collapse and instability of GANs are stil…
GAN image forensicsGenerative Adversarial NetworkImage GenerationGANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
Estimating individualized treatment effects (ITE) is a challenging task due to the need for an individual's potential outcomes to be learned from biased data and without having access to the counterfactuals. We propose a…
Causal InferencecounterfactualGeneralized Dual Discriminator GANs
Dual discriminator generative adversarial networks (D2 GANs) were introduced to mitigate the problem of mode collapse in generative adversarial networks. In D2 GANs, two discriminators are employed alongside a generator:…