SCGAN: Disentangled Representation Learning by Adding Similarity Constraint on Generative Adversarial Nets
IEEE Access, pp. 1-1, 2018.
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
Full Text (Upload PDF)
PPT (Upload PPT)