Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks

Learning to transfer visual attributes requires supervision dataset. Corresponding images with varying attribute values with the same identity are required for learning the transfer function. This largely limits their applications, because capturing them is often a difficult task. To address the issue, we propose an unsupervised method to learn to transfer visual attribute. The proposed method can learn the transfer function without any corresponding images. Inspecting visualization results from various unsupervised attribute transfer tasks, we verify the effectiveness of the proposed method.

• Publication year

  2017

• Venue

  arXiv.org

• Publication date

  2017-07-31

• Fields of study

  Computer Science

• Identifiers
  arXiv 1707.09798
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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