U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

ICLR 2020 Junho KimMinjae KimHyeonwoo KangKwanghee Lee

We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier... (read more)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Image-to-Image Translation anime-to-selfie U-GAT-IT Kernel Inception Distance 11.52 # 2
Image-to-Image Translation cat2dog U-GAT-IT Kernel Inception Distance 7.07 # 1
Image-to-Image Translation dog2cat U-GAT-IT Kernel Inception Distance 8.15 # 1
Image-to-Image Translation horse2zebra U-GAT-IT Kernel Inception Distance 7.06 # 1
Image-to-Image Translation photo2portrait U-GAT-IT Kernel Inception Distance 1.79 # 1
Image-to-Image Translation photo2vangogh U-GAT-IT Kernel Inception Distance 4.28 # 1
Image-to-Image Translation portrait2photo U-GAT-IT Kernel Inception Distance 1.69 # 1
Image-to-Image Translation selfie-to-anime U-GAT-IT Kernel Inception Distance 11.61 # 2
Image-to-Image Translation vangogh2photo U-GAT-IT Kernel Inception Distance 5.61 # 1
Image-to-Image Translation zebra2horse U-GAT-IT Kernel Inception Distance 7.47 # 1