StarGAN v2: Diverse Image Synthesis for Multiple Domains

A good image-to-image translation model should learn a mapping between different visual domains while satisfying the following properties: 1) diversity of generated images and 2) scalability over multiple domains. Existing methods address either of the issues, having limited diversity or multiple models for all domains... (read more)

PDF Abstract CVPR 2020 PDF CVPR 2020 Abstract

Datasets


Introduced in the Paper:

AFHQ

Mentioned in the Paper:

CelebA-HQ FFHQ

Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Image-to-Image Translation AFHQ StarGAN v2 FID 24.4 # 1
LPIPS 0.524 # 1
Multimodal Unsupervised Image-To-Image Translation AFHQ StarGAN v2 FID 16.2 # 1
Multimodal Unsupervised Image-To-Image Translation CelebA-HQ StarGAN v2 FID 13.73 # 1
Image-to-Image Translation CelebA-HQ StarGAN v2 FID 13.73 # 1
LPIPS 0.428 # 1
Fundus to Angiography Generation Fundus Fluorescein Angiogram Photographs & Colour Fundus Images of Diabetic Patients StarGAN-v2 FID 27.7 # 5
Kernel Inception Distance 0.00118 # 3

Methods used in the Paper


METHOD TYPE
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