MirrorGAN: Learning Text-to-image Generation by Redescription

CVPR 2019 Tingting QiaoJing ZhangDuanqing XuDacheng Tao

Generating an image from a given text description has two goals: visual realism and semantic consistency. Although significant progress has been made in generating high-quality and visually realistic images using generative adversarial networks, guaranteeing semantic consistency between the text description and visual content remains very challenging... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Text-to-Image Generation COCO MirrorGAN Inception score 26.47 # 4
Text-to-Image Generation CUB MirrorGAN Inception score 4.56 # 3