TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Uncropping	Places2 val	Boundless	FID	11.8	# 2
Uncropping	Places2 val	Boundless	PD	129.3	# 2
Uncropping	Places2 val	Boundless	Fool rate	20.7	# 2

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Boundless: Generative Adversarial Networks for Image Extension

ICCV 2019 · Piotr Teterwak, Aaron Sarna, Dilip Krishnan, Aaron Maschinot, David Belanger, Ce Liu, William T. Freeman ·

Image extension models have broad applications in image editing, computational photography and computer graphics. While image inpainting has been extensively studied in the literature, it is challenging to directly apply the state-of-the-art inpainting methods to image extension as they tend to generate blurry or repetitive pixels with inconsistent semantics. We introduce semantic conditioning to the discriminator of a generative adversarial network (GAN), and achieve strong results on image extension with coherent semantics and visually pleasing colors and textures. We also show promising results in extreme extensions, such as panorama generation.