SCGAN: Disentangled Representation Learning by Adding Similarity Constraint on Generative Adversarial Nets

IEEE Access, pp. 1-1, 2018.

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Abstract:

We proposed a novel Generative Adversarial Net called SCGAN, which is capable of learning the disentangled representation in a completely unsupervised manner. Inspired by the smoothness assumption and our assumption on the content and the representation of images, we design an effective similarity constraint. SCGAN can disentangle interpr...More

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