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Disentangled representation learning finds compact, independent and easy-to-interpret factors of the data.
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
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Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex
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Learning the parts of objects by non-negative matrix factorization
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Lie groups and lie algebras
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Independent component analysis: algorithms and applications
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Separating style and content with bilinear models
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Decomposed eigenface for face recognition under various lighting conditions
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Generative adversarial nets
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Adam: A method for stochastic optimization
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Stochastic Backpropagation and Approximate Inference in Deep Generative Models
D. Rezende, S. Mohamed, and D. Wierstra · 2014
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Importance weighted autoencoders
Y. Burda, R. B. Grosse, and R. Salakhutdinov · 2015
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Highly-expressive spaces of well-behaved transformations: Keeping it simple
Inverse compositional spatial transformer networks
C. Lin and S. Lucey · 2016
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Latent Space Oddity: on the Curvature of Deep Generative Models
G. Arvanitidis, L. K. Hansen, and S. Hauberg · 2017
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β \beta -vae: Learning basic visual concepts with a constrained variational framework
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner · 2017
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Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
A. Kumar, P. Sattigeri, and A. Balakrishnan · 2017
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dsprites: Disentanglement testing sprites dataset
L. Matthey, I. Higgins, D. Hassabis, and A. Lerchner · 2017
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O. Freifeld, S. Hauberg, K. Batmanghelich, and J. W. F. III · 2015
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M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu · 2015
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Deep convolutional inverse graphics network
T. D. Kulkarni, W. Whitney, P. Kohli, and J. B. Tenenbaum · 2015
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Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
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SMPL: A skinned multi-person linear model
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
S. M. A. Eslami, N. Heess, T. Weber, Y. Tassa, D. Szepesvari, K. Kavukcuoglu, and G. E. Hinton · 2016
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Destnet: Densely fused spatial transformer networks
R. Annunziata, C. Sagonas, and J. Calì · 2018
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Isolating Sources of Disentanglement in Variational Autoencoders
R. T. Q. Chen, X. Li, R. Grosse, and D. Duvenaud · 2018
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Deep diffeomorphic transformer networks
N. S. Detlefsen, O. Freifeld, and S. Hauberg · 2018
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Towards a Definition of Disentangled Representations
I. Higgins, D. Amos, D. Pfau, S. Racaniere, L. Matthey, D. Rezende, and A. Lerchner · 2018
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Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
A. Hyvärinen, H. Sasaki, and R. E. Turner · 2018
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ST-GAN: Spatial Transformer Generative Adversarial Networks for Image Compositing
C.-H. Lin, E. Yumer, O. Wang, E. Shechtman, and S. Lucey · 2018
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A Framework for the Quantitative Evaluation of Disentangled Representations, feb 2019
C. Eastwood and C. K. I. Williams · 2019
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Challenging common assumptions in the unsupervised learning of disentangled representations
F. Locatello, S. Bauer, M. Lucic, S. Gelly, B. Schölkopf, and O. Bachem · 2019
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Unsupervised Part-Based Disentangling of Object Shape and Appearance
D. Lorenz, L. Bereska, T. Milbich, and B. Ommer · 2019
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Deformable Generator Network: Unsupervised Disentanglement of Appearance and Geometry
X. Xing, R. Gao, T. Han, S.-C. Zhu, and Y. Nian Wu · 2019
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