generative adversarial networks - yezhou yang...generative adversarial networks1 •two networks...
TRANSCRIPT
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Generative Adversarial NetworksSTEPHEN MCALEER
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Outline
1. Discriminative vs. generative models
2. Deep generative models
3. Generative adversarial networks
4. Applications and further work
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Discriminative vs. Generative Models
𝑥₂
duck
car
tree
Training set𝒙, 𝒚
Discriminative Model𝒑(𝒚|𝒙)
pickup truck
Generative Model𝒑(𝒙, 𝒚)
𝑧 ~ 𝑁(0,1)
horse
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Deep Learning
• Learn hierarchical model of data
• Higher-level features derived from lower-level features
• Has achieved much success in discriminative tasks
𝑥₂
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Generating an Image
• Opposite of convolutional neural nets
• How to train it?
𝑥₂
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Deep Generative Models
• Variational Autoencoders1
• Generative Adversarial Networks2
1. Kingma, Diederik P and Welling, Max. Auto-Encoding Variational Bayes. In The 2nd International Conference on Learning Representations (ICLR), 2013.
2. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, pages 2672–2680. 2014.
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Generative Adversarial Networks1
• Two networks compete with one another
• The generator generates imitations of data
• The discriminator distinguishes generated data from real data
• Backwards-differentiable(!)
1. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, pages 2672–2680. 2014.
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Generative Adversarial Networks1
1. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, pages 2672–2680. 2014.
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Results
Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
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Results
Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015.
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Results
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee. Generative adversarial text-to-image synthesis. In ICML, 2016b.
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Further Research
• Disentangled representations
• Different architectures
• Combining GANs with other models
• Finding minima in non-convex games
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Sources
• I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, pages 2672–2680. 2014.
• Kingma, Diederik P and Welling, Max. Auto-Encoding Variational Bayes. In The 2nd International Conference on Learning Representations (ICLR), 2013.
• Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXivpreprint arXiv:1511.06434, 2015.
• S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee. Generative adversarial text-to-image synthesis. In ICML, 2016b.
• A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu. Pixel recurrent neural networks. arXiv preprint arXiv:1601.06759, 2016.