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 @math 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 @math 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 @math GAN achieves superior model capacity and better generation performance comparing with existing image-to-image translation GAN models.
There are only very few recent methods attempting to implement multi-modal image-to-image translation in an efficient way. Anoosheh al propose a ComboGAN model @cite_38 , which only needs to train @math generator discriminator pairs for @math different image domains. To further reduce the model complexity, Choi al introduce StarGAN @cite_25 , which has a single generator discriminator pair and is able to perform the task with a complexity of @math . Although the model complexity is low, jointly learning both the translation and reconstruction tasks with the same generator requires the sharing of all parameters, which increases the optimization complexity and reduces the generalization ability, thus leading to unsatisfactory generation performance. The proposed approach aims at obtaining a good balance between the model capacity and the generation quality. Along this research line, we propose a Dual Generator Generative Adversarial Network (G @math GAN), which achieves this target via using two task-specific generators and one discriminator. We also explore various optimization objectives to train better the model to produce more consistent and more stable results.
@cite_38 @cite_25
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The past year alone has seen unprecedented leaps in the area of learning-based image translation, namely Cycle-GAN, by But experiments so far have been tailored to merely two domains at a time, and scaling them to more would require an quadratic number of models to be trained. And with two-domain models taking days to train on current hardware, the number of domains quickly becomes limited by the time and resources required to process them. In this paper, we propose a multi-component image translation model and training scheme which scales linearly - both in resource consumption and time required - with the number of domains. We demonstrate its capabilities on a dataset of paintings by 14 different artists and on images of the four different seasons in the Alps. Note that 14 data groups would need (14 choose 2) = 91 different CycleGAN models: a total of 182 generator discriminator pairs; whereas our model requires only 14 generator discriminator pairs. 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.