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ALICE: Towards understanding adversarial learning for joint distribution matching

Publication ,  Conference
Li, C; Liu, H; Chen, C; Pu, Y; Chen, L; Henao, R; Carin, L
Published in: Advances in Neural Information Processing Systems
January 1, 2017

We investigate the non-identifiability issues associated with bidirectional adversarial training for joint distribution matching. Within a framework of conditional entropy, we propose both adversarial and non-adversarial approaches to learn desirable matched joint distributions for unsupervised and supervised tasks. We unify a broad family of adversarial models as joint distribution matching problems. Our approach stabilizes learning of unsupervised bidirectional adversarial learning methods. Further, we introduce an extension for semi-supervised learning tasks. Theoretical results are validated in synthetic data and real-world applications.

Duke Scholars

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2017

Volume

2017-December

Start / End Page

5496 / 5504

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology
 

Citation

APA
Chicago
ICMJE
MLA
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Li, C., Liu, H., Chen, C., Pu, Y., Chen, L., Henao, R., & Carin, L. (2017). ALICE: Towards understanding adversarial learning for joint distribution matching. In Advances in Neural Information Processing Systems (Vol. 2017-December, pp. 5496–5504).
Li, C., H. Liu, C. Chen, Y. Pu, L. Chen, R. Henao, and L. Carin. “ALICE: Towards understanding adversarial learning for joint distribution matching.” In Advances in Neural Information Processing Systems, 2017-December:5496–5504, 2017.
Li C, Liu H, Chen C, Pu Y, Chen L, Henao R, et al. ALICE: Towards understanding adversarial learning for joint distribution matching. In: Advances in Neural Information Processing Systems. 2017. p. 5496–504.
Li, C., et al. “ALICE: Towards understanding adversarial learning for joint distribution matching.” Advances in Neural Information Processing Systems, vol. 2017-December, 2017, pp. 5496–504.
Li C, Liu H, Chen C, Pu Y, Chen L, Henao R, Carin L. ALICE: Towards understanding adversarial learning for joint distribution matching. Advances in Neural Information Processing Systems. 2017. p. 5496–5504.

Published In

Advances in Neural Information Processing Systems

ISSN

1049-5258

Publication Date

January 1, 2017

Volume

2017-December

Start / End Page

5496 / 5504

Related Subject Headings

  • 4611 Machine learning
  • 1702 Cognitive Sciences
  • 1701 Psychology