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Learning Clusterable Visual Features for Zero-Shot Recognition
In zero-shot learning (ZSL), conditional generators have been widely use...
10/07/2020 ∙ by Jingyi Xu, et al. ∙
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Self-supervised Deformation Modeling for Facial Expression Editing
Recent advances in deep generative models have demonstrated impressive r...
11/02/2019 ∙ by ShahRukh Athar, et al. ∙
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Lifting AutoEncoders: Unsupervised Learning of a Fully-Disentangled 3D Morphable Model using Deep Non-Rigid Structure from Motion
In this work we introduce Lifting Autoencoders, a generative 3D surface-...
04/26/2019 ∙ by Mihir Sahasrabudhe, et al. ∙
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Latent Space Optimal Transport for Generative Models
Variational Auto-Encoders enforce their learned intermediate latent-spac...
09/16/2018 ∙ by Huidong Liu, et al. ∙
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Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
In this work we introduce Deforming Autoencoders, a generative model for...
06/18/2018 ∙ by Zhixin Shu, et al. ∙
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An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations
Several factors contribute to the appearance of an object in a visual sc...
11/28/2017 ∙ by Mengjiao Wang, et al. ∙
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Improving Heterogeneous Face Recognition with Conditional Adversarial Networks
Heterogeneous face recognition between color image and depth image is a ...
09/08/2017 ∙ by Wuming Zhang, et al. ∙
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