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STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
[article]
2019
arXiv
pre-print
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. Moreover, existing methods exploit target attribute vector to guide the flexible translation to desired target domain. In this work, we suggest to address
arXiv:1904.09709v1
fatcat:7wtborudqncj7e4dnkznowg6ca