AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
This work addresses image alignment for cross-domain applications, but it appears incremental as it builds on existing conditional GAN approaches.
The authors tackled the problem of aligning cross-domain images without paired samples by improving conditional GAN-based methods, achieving successful alignment across numerous tasks.
Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-domain images. One of the methods is to use conditional GAN for alignment. However, previous attempts of adopting conditional GAN do not perform as well as other methods. In this work we present an approach for improving the capability of the methods which are based on conditional GAN. We evaluate the proposed method on numerous tasks and the experimental results show that it is able to align the cross-domain images successfully in absence of paired samples. Furthermore, we also propose another model which conditions on multiple information such as domain information and label information. Conditioning on domain information and label information, we are able to conduct label propagation from the source domain to the target domain. A 2-step alternating training algorithm is proposed to learn this model.