Disentangling GAN with One-Hot Sampling and Orthogonal Regularization

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Liu, Bingchen; Zhu, Yizhe; Fu, Zuohui; de Melo, Gerard; Elgammal, Ahmed;
(2019)
  • Subject: Computer Science - Computer Vision and Pattern Recognition

Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN). While previous works mostly attempt to tackle disentanglement learning... View more
  • References (37)
    37 references, page 1 of 4

    [1] Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8):1798-1828, 2013.

    [2] Hyunjik Kim and Andriy Mnih. Disentangling by factorising. arXiv preprint arXiv:1802.05983, 2018.

    [3] Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum, and Samuel J Gershman. Building machines that learn and think like people. Behavioral and brain sciences, 40, 2017.

    [4] Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. beta-vae: Learning basic visual concepts with a constrained variational framework. In International Conference on Learning Representations, 2017.

    [5] 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, pages 2672-2680, 2014.

    [6] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.

    [7] Andrew Michael Saxe, Yamini Bansal, Joel Dapello, Madhu Advani, Artemy Kolchinsky, Brendan Daniel Tracey, and David Daniel Cox. On the information bottleneck theory of deep learning. 2018.

    [9] Marylou Gabrié, Andre Manoel, Clément Luneau, Nicolas Macris, Florent Krzakala, Lenka Zdeborová, et al. Entropy and mutual information in models of deep neural networks. In Advances in Neural Information Processing Systems, pages 1826-1836, 2018.

    [10] Alex Alemi, Ben Poole, Ian Fischer, Josh Dillon, Rif A. Saurus, and Kevin Murphy. An informationtheoretic analysis of deep latent-variable models, 2018.

    [11] Christos Louizos, Karen Ullrich, and Max Welling. Bayesian compression for deep learning. In Advances in Neural Information Processing Systems, pages 3288-3298, 2017.

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