Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networks

Abstract

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.

References in corpus (5)

• Auto-Encoding Variational Bayes
• Conditional Generative Adversarial Nets
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
• Deep Convolutional Inverse Graphics Network

Cited by in corpus (6)

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• Learning Compositional Visual Concepts with Mutual Consistency
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