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Deep neural networks can form high-level hierarchical representations ofinput data. Various researchers have demonstrated that these representationscan be used to enable a variety of useful applications. However, suchrepresentations are typically based on the statistics within the data, and maynot conform with the semantic representation that may be necessitated by theapplication. Conditional models are typically used to overcome this challenge,but they require large annotated datasets which are difficult to come by andcostly to create. In this paper, we show that semantically-alignedrepresentations can be generated instead with the help of a physics basedengine. This is accomplished by creating a synthetic dataset with decoupledattributes, learning an encoder for the synthetic dataset, and augmentingprescribed attributes from the synthetic domain with attributes from the realdomain. It is shown that the proposed (SYNTH-VAE-GAN) method can construct aconditional predictive-generative model of human face attributes withoutrelying on real data labels.

Assembling Semantically-Disentangled Representations for Predictive-Generative Models via Adaptation from Synthetic Domain