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Multimodal variational autoencoders (VAEs) have shown promise as efficient generative models for weakly-supervised data.
DIVA: domain invariant variational autoencoders
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Multimodal generative models for compositional representation learning
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Gradient-based learning applied to document recognition
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Multimodal deep learning
Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., and Ng, A. Y. (2011) · 2011
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The Caltech-UCSD Birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011) · 2011
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Elements of information theory
Cover, T. M. and Thomas, J. A. (2012) · 2012
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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) · 2014
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
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Multimodal learning with deep Boltzmann machines
Srivastava, N. and Salakhutdinov, R. (2014) · 2014
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Adam: a method for stochastic gradient descent
Kingma, D. P. and Ba, J. (2015) · 2015
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Joint multimodal learning with deep generative models
Suzuki, M., Nakayama, K., and Matsuo, Y. (2016) · 2016
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Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K. (2017) · 2017
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GANs trained by a two time-scale update rule converge to a local Nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017) · 2017
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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. (2017) · 2017
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StarGAN: unified generative adversarial networks for multi-domain image-to-image translation
Choi, Y., Choi, M., Kim, M., Ha, J.-W., Kim, S., and Choo, J. (2018) · 2018
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Disentangling by partitioning: a representation learning framework for multimodal sensory data
Hsu, W.-N. and Glass, J. (2018) · 2018
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Multimodal unsupervised image-to-image translation
Huang, X., Liu, M., Belongie, S. J., and Kautz, J. (2018) · 2018
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Generative models of visually grounded imagination
Vedantam, R., Fischer, I., Huang, J., and Murphy, K. (2018) · 2018
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Multimodal generative models for scalable weakly-supervised learning
Wu, M. and Goodman, N. (2018) · 2018
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Multimodal machine learning: a survey and taxonomy
Learning factorized multimodal representations
Tsai, Y.-H. H., Liang, P. P., Zadeh, A., Morency, L.-P., and Salakhutdinov, R. (2019) · 2019
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Doubly reparameterized gradient estimators for Monte Carlo objectives
Tucker, G., Lawson, D., Gu, S., and Maddison, C. J. (2019) · 2019
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Self-supervised disentanglement of modality-specific and shared factors improves multimodal generative models
Daunhawer, I., Sutter, T. M., Marcinkevics, R., and Vogt, J. E. (2020) · 2020
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Weakly-supervised disentanglement without compromises
Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., and Tschannen, M. (2020) · 2020
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Multimodal generative learning utilizing Jensen-Shannon-divergence
Sutter, T. M., Daunhawer, I., and Vogt, J. E. (2020) · 2020
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Inverse learning of symmetry transformations
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Baltrušaitis, T., Ahuja, C., and Morency, L.-P. (2019) · 2019
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Pros and cons of GAN evaluation measures
Borji, A. (2019) · 2019
Cited alongside, same era.
Hetero-modal variational encoder-decoder for joint modality completion and segmentation
Dorent, R., Joutard, S., Modat, M., Ourselin, S., and Vercauteren, T. (2019) · 2019
Cited alongside, same era.
The incomplete Rosetta stone problem: identifiability results for multi-view nonlinear ICA
Gresele, L., Rubenstein, P. K., Mehrjou, A., Locatello, F., and Schölkopf, B. (2019) · 2019
Cited alongside, same era.
Multi-source neural variational inference
Kurle, R., Guennemann, S., and van der Smagt, P. (2019) · 2019
Cited alongside, same era.
Few-shot unsupervised image-to-image translation
Liu, M., Huang, X., Mallya, A., Karras, T., Aila, T., Lehtinen, J., and Kautz, J. (2019) · 2019
Cited alongside, same era.
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. (2019) · 2019
Cited alongside, same era.
Wieser, M., Parbhoo, S., Wieczorek, A., and Roth, V. (2020) · 2020
Later among the works it cites.
A variational information bottleneck approach to multi-omics data integration
Lee, C. and van der Schaar, M. (2021) · 2021
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M6: a chinese multimodal pretrainer
Lin, J., Men, R., Yang, A., Zhou, C., Ding, M., Zhang, Y., Wang, P., Wang, A., Jiang, L., Jia, X., et al. (2021) · 2021
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A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data
Minoura, K., Abe, K., Nam, H., Nishikawa, H., and Shimamura, T. (2021) · 2021
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The role of disentanglement in generalisation
Montero, M. L., Ludwig, C. J., Costa, R. P., Malhotra, G., and Bowers, J. (2021) · 2021
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Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I. (2021) · 2021
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Relating by contrasting: a data-efficient framework for multimodal generative models
Shi, Y., Paige, B., Torr, P., and Siddharth, N. (2021) · 2021
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Generalized multimodal ELBO
Sutter, T. M., Daunhawer, I., and Vogt, J. E. (2021) · 2021
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Self-supervised learning with data augmentations provably isolates content from style
von Kügelgen, J., Sharma, Y., Gresele, L., Brendel, W., Schölkopf, B., Besserve, M., and Locatello, F. (2021) · 2021
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