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A generative model with a disentangled representation allows for independent control over different aspects of the output.
Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G · 2012
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
Aubry, M., Maturana, D., Efros, A. A., Russell, B. C., and Sivic, J · 2014
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P · 2016
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Automatic differentiation in machine learning: a survey
Baydin, A. G., Pearlmutter, B. A., Radul, A. A., and Siskind, J. M · 2017
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On the effects of batch and weight normalization in generative adversarial networks
Xiang, S. and Li, H · 2017
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2018
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Understanding disentangling in β \beta -vae
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A · 2018
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Isolating sources of disentanglement in variational autoencoders
Chen, T. Q., Li, X., Grosse, R., and Duvenaud, D · 2018
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Structured disentangled representations
Esmaeili, B., Wu, H., Jain, S., Bozkurt, A., Siddharth, N., Paige, B., Brooks, D. H., Dy, J., and van de Meent, J.-W · 2018
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Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
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dsprites: Disentanglement testing sprites dataset
Matthey, L., Higgins, I., Hassabis, D., and Lerchner, A · 2017
Cited alongside, same era.
A new trick for calculating jacobian vector products
Townsend, J · 2017
Cited alongside, same era.
Kim, H. and Mnih, A · 2018
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Which training methods for gans do actually converge?
Mescheder, L., Geiger, A., and Nowozin, S · 2018
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Spectral normalization for generative adversarial networks
Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y · 2018
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