Joint Multimodal Learning with Deep Generative Models

Preprint English OPEN
Suzuki, Masahiro; Nakayama, Kotaro; Matsuo, Yutaka;
(2016)
  • Subject: Statistics - Machine Learning | Computer Science - Learning

We investigate deep generative models that can exchange multiple modalities bi-directionally, e.g., generating images from corresponding texts and vice versa. Recently, some studies handle multiple modalities on deep generative models, such as variational autoencoders (... View more
  • References (22)
    22 references, page 1 of 3

    Samuel R Bowman, Luke Vilnis, Oriol Vinyals, Andrew M Dai, Rafal Jozefowicz, and Samy Bengio. Generating sentences from a continuous space. arXiv preprint arXiv:1511.06349, 2015.

    Yuri Burda, Roger Grosse, and Ruslan Salakhutdinov. Importance weighted autoencoders. arXiv preprint arXiv:1509.00519, 2015.

    Sander Dieleman, Jan Schlter, Colin Raffel, Eben Olson, Sren Kaae Snderby, Daniel Nouri, Daniel Maturana, Martin Thoma, Eric Battenberg, Jack Kelly, Jeffrey De Fauw, Michael Heilman, Diogo Moitinho de Almeida, Brian McFee, Hendrik Weideman, Gbor Takcs, Peter de Rivaz, Jon Crall, Gregory Sanders, Kashif Rasul, Cong Liu, Geoffrey French, and Jonas Degrave. Lasagne: First release., August 2015. URL http://dx.doi.org/10.5281/zenodo.27878.

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems, pp. 2672-2680, 2014.

    Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.

    Diederik P Kingma and Max Welling. arXiv:1312.6114, 2013.

    Diederik P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. Semi-supervised learning with deep generative models. In Advances in Neural Information Processing Systems, pp. 3581-3589, 2014.

    Tejas D Kulkarni, William F Whitney, Pushmeet Kohli, and Josh Tenenbaum. Deep convolutional inverse graphics network. In Advances in Neural Information Processing Systems, pp. 2539-2547, 2015.

    Anders Boesen Lindbo Larsen, Søren Kaae Sønderby, and Ole Winther. Autoencoding beyond pixels using a learned similarity metric. arXiv preprint arXiv:1512.09300, 2015.

    Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE International Conference on Computer Vision, pp. 3730-3738, 2015.

  • Related Research Results (1)
    Inferred by OpenAIRE
    software
    Lasagne: First Release. (2015)
    73%
  • Metrics
Share - Bookmark