A variational autoencoder trained jointly with Gaussian process regression can estimate high-dimensional image responses from low-dimensional inputs, though the derivation is heuristic.
Attribute2Image: Conditional Image Generation from Visual Attributes
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abstract
This paper investigates a novel problem of generating images from visual attributes. We model the image as a composite of foreground and background and develop a layered generative model with disentangled latent variables that can be learned end-to-end using a variational auto-encoder. We experiment with natural images of faces and birds and demonstrate that the proposed models are capable of generating realistic and diverse samples with disentangled latent representations. We use a general energy minimization algorithm for posterior inference of latent variables given novel images. Therefore, the learned generative models show excellent quantitative and visual results in the tasks of attribute-conditioned image reconstruction and completion.
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cs.CV 1years
2019 1verdicts
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Variational Autoencoded Regression: High Dimensional Regression of Visual Data on Complex Manifold
A variational autoencoder trained jointly with Gaussian process regression can estimate high-dimensional image responses from low-dimensional inputs, though the derivation is heuristic.