STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing

CVPR, 2019.

Cited by: 37|Bibtex|Views42
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

Arbitrary attribute editing generally can be tackled by incorporating encoder-decoder and generative adversarial networks. However, the bottleneck layer in encoder-decoder usually gives rise to blurry and low quality editing result. And adding skip connections improves image quality at the cost of weakened attribute manipulation ability. ...More

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