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Variational autoencoders (VAEs) are widely used deep generative models capable of learning unsupervised latent representations of data.
The toronto face database
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Disentangling factors of variation in deep representations using adversarial training, 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
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Dna-gan: Learning disentangled representations from multi-attribute images
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Elegant: Exchanging latent encodings with gan for transferring multiple face attributes
Taihong Xiao, Jiapeng Hong, and Jinwen Ma
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Fader networks: Manipulating images by sliding attributes
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Genegan: Learning object transfiguration and attribute subspace from unpaired data
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