| Title | Image Translation based on Attention Residual GAN |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Zhang, Minghao, He, Lingmin, Wang, Xiuhui |
| Conference Name | 2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) |
| Keywords | attention mechanism, Computer vision, Deep Learning, Degradation, distortion, Generative Adversarial Learning, generative adversarial networks, image translation, Metrics, Neural networks, pubcrawl, residual neural network, resilience, Resiliency, Scalability, Training |
| Abstract | Using Generative Adversarial Networks (GAN) to translate images is a significant field in computer vision. There are partial distortion, artifacts and detail loss in the images generated by current image translation algorithms. In order to solve this problem, this paper adds attention-based residual neural network to the generator of GAN. Attention-based residual neural network can improve the representation ability of the generator by weighting the channels of the feature map. Experiment results on the Facades dataset show that Attention Residual GAN can translate images with excellent quality. |
| DOI | 10.1109/ICAICE54393.2021.00156 |
| Citation Key | zhang_image_2021 |
Image Translation based on Attention Residual GAN