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We connect a large class of Generative Deep Networks (GDNs) with spline operators in order to derive their properties, limitations, and new opportunities.
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Variational inference with normalizing flows
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dsprites: Disentanglement testing sprites dataset
Matthey, L., Higgins, I., Hassabis, D., and Lerchner, A · 2017
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Stabilizing training of generative adversarial networks through regularization
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Tomczak, J. M. and Welling, M · 2017
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Zhang, P., Liu, Q., Zhou, D., Xu, T., and He, X · 2017
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Dinh, L., Sohl-Dickstein, J., and Bengio, S · 2016
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Generative multi-adversarial networks
Durugkar, I., Gemp, I., and Mahadevan, S · 2016
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Improving variational auto-encoders using householder flow
Tomczak, J. M. and Welling, M · 2016
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Energy-based generative adversarial network
Zhao, J., Mathieu, M., and LeCun, Y · 2016
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Towards principled methods for training generative adversarial networks. arxiv, 2017
Arjovsky, M. and Bottou, L · 2017
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Ben-Yosef, M. and Weinshall, D · 2018
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Sylvester normalizing flows for variational inference
Berg, R. v. d., Hasenclever, L., Tomczak, J. M., and Welling, M · 2018
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Some theoretical properties of gans
Biau, G., Cadre, B., Sangnier, M., and Tanielian, U · 2018
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Hyperspherical variational auto-encoders
Davidson, T. R., Falorsi, L., De Cao, N., Kipf, T., and Tomczak, J. M · 2018
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Multi-agent diverse generative adversarial networks
Ghosh, A., Kulharia, V., Namboodiri, V. P., Torr, P. H., and Dokania, P. K · 2018
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Ffjord: Free-form continuous dynamics for scalable reversible generative models
Grathwohl, W., Chen, R. T., Betterncourt, J., Sutskever, I., and Duvenaud, D · 2018
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Kim, H. and Mnih, A · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Learning overparameterized neural networks via stochastic gradient descent on structured data
Li, Y. and Liang, Y · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 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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Theory and experiments on vector quantized autoencoders
Roy, A., Vaswani, A., Neelakantan, A., and Parmar, N · 2018
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Perturbation theory approach to study the latent space degeneracy of variational autoencoders, 2019
Andrés-Terré, H. and Lió, P · 2019
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The geometry of deep networks: Power diagram subdivision
Balestriero, R., Cosentino, R., Aazhang, B., and Baraniuk, R · 2019
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Prescribed generative adversarial networks, 2019
Dieng, A. B., Ruiz, F. J. R., Blei, D. M., and Titsias, M. K · 2019
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A rad approach to deep mixture models
Dinh, L., Sohl-Dickstein, J., Pascanu, R., and Larochelle, H · 2019
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Diversity-sensitive conditional generative adversarial networks, 2019
Yang, D., Hong, S., Jang, Y., Zhao, T., and Lee, H · 2019
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