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Deep generative models like variational autoencoders approximate the intrinsic geometry of high dimensional data manifolds by learning low-dimensional latent-space variables and an embedding function.
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Alec Radford, Luke Metz, and Soumith Chintala. 2015 · 2015
Disentangling factors of variation in deep representation using adversarial training. In Advances in Neural Information Processing Systems
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Latent space oddity: on the curvature of deep generative models
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Challenges in disentangling independent factors of variation
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Disentangling Factors of Variation with Cycle-Consistent Variational Auto-Encoders
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Weakly-supervised disentangling with recurrent transformations for 3d view synthesis. In Advances in Neural Information Processing Systems
Jimei Yang, Scott E Reed, Ming-Hsuan Yang, and Honglak Lee. 2015 · 2015
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Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
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Line Kuhnel, Tom Fletcher, Sarang Joshi, and Stefan Sommer. 2018 · 2018
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Feature-Based Metrics for Exploring the Latent Space of Generative Models
Samuli Laine. 2018 · 2018
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The Riemannian Geometry of Deep Generative Models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops
Hang Shao, Abhishek Kumar, and P Thomas Fletcher. 2018 · 2018
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