Abstract
Unpaired image translation is a challenging problem in computer vision, while existing generative adversarial networks (GANs) models mainly use the adversarial loss and other constraints to model. But the degree of constraint imposed on the generator and the discriminator is not enough, which results in bad image quality. In addition, we find that the current GANs‐based models have not yet been implemented by adding an auxiliary domain, which is used to constrain the generator. To solve the problem mentioned above, we propose a multiscale and multilevel GANs (MMGANs) model for image translation. In this model, we add an auxiliary domain to constrain generator, which combines this auxiliary domain with the original domains for modelling and helps generator learn the detailed content of the image. Then we use multiscale and multilevel feature matching to constrain the discriminator. The purpose is to make the training process as stable as possible. Finally, we conduct experiments on six image translation tasks. The results verify the validity of the proposed model.
Highlights
Image translation [1] is similar to language translation, which converts the input image of source domain to a corresponding image of target domain
We focus on the unpaired image translation task based on the method of generative adversarial networks (GANs)
The experiment focuses on two points: (1) compared with CycleGAN, DualGAN, UNIT, and multiscale and multilevel GANs (MMGANs), we show generated images in different datasets and (2) we use our comprehensive performance evaluation method to calculate a quantitative ratio and analyze it
Summary
Image translation [1] is similar to language translation, which converts the input image of source domain to a corresponding image of target domain. There are many methods [1,2,3,4,5,6] to solve image translation problem, but GANs based methods [1, 7,8,9,10,11] have gained increasing attention in the image translation. In the methods of GANs, they view the input image of source domain as the input of generator, which generates fake samples to deceive discriminator. According to whether the datasets are paired or unpaired, the image translation of GANs based methods can be roughly classified into two categories: paired and unpaired image translation. To reduce the cost of obtaining paired training datasets, [20,21,22] propose the unpaired methods, which are unsupervised domain translation methods [23]
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