publication . Preprint . 2018

Flipped-Adversarial AutoEncoders

Zhang, Jiyi; Dang, Hung; Lee, Hwee Kuan; Chang, Ee-Chien;
Open Access English
  • Published: 13 Feb 2018
Abstract
We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously trains a generative model G that maps an arbitrary latent code distribution to a data distribution and an encoder E that embodies an "inverse mapping" that encodes a data sample into a latent code vector. Unlike previous hybrid approaches that leverage adversarial training criterion in constructing autoencoders, FAAE minimizes re-encoding errors in the latent space and exploits adversarial criterion in the data space. Experimental evaluations demonstrate that the proposed framework produces sharper reconstructed images while at the same time enabling inference that captures rich semantic rep...
Subjects
arXiv: Statistics::Machine Learning
free text keywords: Computer Science - Learning
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19 references, page 1 of 2

Chang, Ming-Wei, Ratinov, Lev-Arie, Roth, Dan, and Srikumar, Vivek. Importance of semantic representation: Dataless classification. In AAAI, volume 2, pp. 830-835, 2008.

Dang, Hung, Huang, Yue, and Chang, Ee-Chien. Evading classifiers by morphing in the dark. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 119-133. ACM, 2017.

Denton, Emily L, Chintala, Soumith, Fergus, Rob, et al. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in neural information processing systems, pp. 1486-1494, 2015. [OpenAIRE]

Donahue, Jeff, Kra¨henbu¨hl, Philipp, and Darrell, Trevor. Adversarial feature learning. arXiv preprint arXiv:1605.09782, 2016.

Dumoulin, Vincent, Belghazi, Ishmael, Poole, Ben, Lamb, Alex, Arjovsky, Martin, Mastropietro, Olivier, and Courville, Aaron. Adversarially learned inference. arXiv preprint arXiv:1606.00704, 2016. [OpenAIRE]

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

Griffiths, Thomas L, Steyvers, Mark, and Tenenbaum, Joshua B. Topics in semantic representation. Psychological review, 114(2):211, 2007. [OpenAIRE]

Huang, Xun, Li, Yixuan, Poursaeed, Omid, Hopcroft, John, and Belongie, Serge. Stacked generative adversarial networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pp. 4, 2017.

Ioffe, Sergey and Szegedy, Christian. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448-456, 2015.

Jaiswal, Ayush, AbdAlmageed, Wael, Wu, Yue, and Natarajan, Premkumar. Bidirectional conditional generative adversarial networks. arXiv preprint arXiv:1711.07461, 2017.

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

Li, Zhigang and Luo, Yupin. Generate identity-preserving faces by generative adversarial networks. arXiv preprint arXiv:1706.03227, 2017. [OpenAIRE]

Lipton, Zachary C and Tripathi, Subarna. Precise recovery of latent vectors from generative adversarial networks. arXiv preprint arXiv:1702.04782, 2017. [OpenAIRE]

Makhzani, Alireza, Shlens, Jonathon, Jaitly, Navdeep, Goodfellow, Ian, and Frey, Brendan. Adversarial autoencoders. arXiv preprint arXiv:1511.05644, 2015.

Mirza, Mehdi and Osindero, Simon. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014. [OpenAIRE]

19 references, page 1 of 2
Related research
Abstract
We propose a flipped-Adversarial AutoEncoder (FAAE) that simultaneously trains a generative model G that maps an arbitrary latent code distribution to a data distribution and an encoder E that embodies an "inverse mapping" that encodes a data sample into a latent code vector. Unlike previous hybrid approaches that leverage adversarial training criterion in constructing autoencoders, FAAE minimizes re-encoding errors in the latent space and exploits adversarial criterion in the data space. Experimental evaluations demonstrate that the proposed framework produces sharper reconstructed images while at the same time enabling inference that captures rich semantic rep...
Subjects
arXiv: Statistics::Machine Learning
free text keywords: Computer Science - Learning
Download from
19 references, page 1 of 2

Chang, Ming-Wei, Ratinov, Lev-Arie, Roth, Dan, and Srikumar, Vivek. Importance of semantic representation: Dataless classification. In AAAI, volume 2, pp. 830-835, 2008.

Dang, Hung, Huang, Yue, and Chang, Ee-Chien. Evading classifiers by morphing in the dark. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 119-133. ACM, 2017.

Denton, Emily L, Chintala, Soumith, Fergus, Rob, et al. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in neural information processing systems, pp. 1486-1494, 2015. [OpenAIRE]

Donahue, Jeff, Kra¨henbu¨hl, Philipp, and Darrell, Trevor. Adversarial feature learning. arXiv preprint arXiv:1605.09782, 2016.

Dumoulin, Vincent, Belghazi, Ishmael, Poole, Ben, Lamb, Alex, Arjovsky, Martin, Mastropietro, Olivier, and Courville, Aaron. Adversarially learned inference. arXiv preprint arXiv:1606.00704, 2016. [OpenAIRE]

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

Griffiths, Thomas L, Steyvers, Mark, and Tenenbaum, Joshua B. Topics in semantic representation. Psychological review, 114(2):211, 2007. [OpenAIRE]

Huang, Xun, Li, Yixuan, Poursaeed, Omid, Hopcroft, John, and Belongie, Serge. Stacked generative adversarial networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pp. 4, 2017.

Ioffe, Sergey and Szegedy, Christian. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448-456, 2015.

Jaiswal, Ayush, AbdAlmageed, Wael, Wu, Yue, and Natarajan, Premkumar. Bidirectional conditional generative adversarial networks. arXiv preprint arXiv:1711.07461, 2017.

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

Li, Zhigang and Luo, Yupin. Generate identity-preserving faces by generative adversarial networks. arXiv preprint arXiv:1706.03227, 2017. [OpenAIRE]

Lipton, Zachary C and Tripathi, Subarna. Precise recovery of latent vectors from generative adversarial networks. arXiv preprint arXiv:1702.04782, 2017. [OpenAIRE]

Makhzani, Alireza, Shlens, Jonathon, Jaitly, Navdeep, Goodfellow, Ian, and Frey, Brendan. Adversarial autoencoders. arXiv preprint arXiv:1511.05644, 2015.

Mirza, Mehdi and Osindero, Simon. Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784, 2014. [OpenAIRE]

19 references, page 1 of 2
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