Attention-Guided Generative Adversarial Networks for Unsupervised Image-to-Image Translation

28 Mar 2019 Hao Tang Dan Xu Nicu Sebe Yan Yan

The state-of-the-art approaches in Generative Adversarial Networks (GANs) are able to learn a mapping function from one image domain to another with unpaired image data. However, these methods often produce artifacts and can only be able to convert low-level information, but fail to transfer high-level semantic part of images... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Facial Expression Translation AR Face AGGAN AMT 12.8 # 1
PSNR 14.9187 # 1
MSE 25.086 # 1
Facial Expression Translation Bu3dfe AGGAN AMT 32.9 # 1
MSE 5.745 # 1
PSNR 21.3247 # 1
Facial Expression Translation CelebA AGGAN AMT 38.9 # 1

Methods used in the Paper


METHOD TYPE
🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet