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PixelGAN Autoencoders

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Alireza Makhzani, Brendan Frey Machine learning Group University of Toronto CIFAR Deep Learning Summer School Montreal, Canada June 29th, 2017

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Alireza Makhzani PixelGAN Autoencoders / 27 CIFAR Deep Learning Summer School Montreal, Canada Machine learning Group University of Toronto PixelGAN Autoencoders Alireza Makhzani, Brendan Frey June 29th, 2017 1
Alireza Makhzani PixelGAN Autoencoders / 27 Outline 2 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors ✦ Clustering ✦ Semi-supervised Learning
Alireza Makhzani PixelGAN Autoencoders / 27 PixelCNNs 33 ✦ Learn the image statistics directly at the pixel level. ✦ Good at modelling low-level pixel statistics. ✦ Samples lack global structure. ✦ Lacking latent representation. ✦ Conditional PixelCNNs can learn conditional densities. van den Oord et al., 2016
Alireza Makhzani PixelGAN Autoencoders / 27 Variational Autoencoders 4 log p(x) > Eq(z|x)[ log(p(x|z)] KL(q(z|x)kp(z)) 4 Kingma et al., 2013 ✦ Good at capturing the global structure, but samples are blurry. ✦ Learn hierarchical representations useful for down-stream tasks. ✦ Attempts at combining PixelCNN with VAEs: • PixelVAE (Gulrajani et al., 2016) • VLAE (Chen et al., 2017)
Alireza Makhzani PixelGAN Autoencoders / 27 Adversarial Autoencoders 5 A B C D Code Space of MNIST: Gaussian Prior Mixture of Gaussians Makhzani et al., 2015
Alireza Makhzani PixelGAN Autoencoders / 27 Outline 6 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors ✦ Clustering ✦ Semi-supervised Learning
Alireza Makhzani PixelGAN Autoencoders / 27 Limitations of Variational/Adversarial Autoencoders 7 ✦ All the image statistics are captured by the single latent vector. label, style global and local VAE None z x p(z) p(x|z) Latent Variable Deterministic (factorized Gaussians)
Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 8 ✦ The image statistics are captured jointly by the latent vector and the autoregressive decoder. PixelCNN Latent Variablez x p(z) p(x|z)
Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 9 PixelCNN Latent Variablez x p(z) p(x|z) GAN ✦ The image statistics are captured jointly by the latent vector and the autoregressive decoder.
Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 10 PixelGAN (Gaussian) Global (low-frequency) Local (high-frequency) PixelGAN (Categorical) Discrete (label) Continuous (Style) PixelCNN Latent Variablez x p(z) p(x|z) GAN ✦ The image statistics are captured jointly by the latent vector and the autoregressive decoder.
Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 11 Semi-supervised Learning PixelCNN Latent Variablez x p(z) p(x|z) GAN PixelGAN (Gaussian) Global (low-frequency) Local (high-frequency) PixelGAN (Categorical) Discrete (label) Continuous (Style) ✦ The image statistics are captured jointly by the latent vector and the autoregressive decoder.
Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 12 Cost function of PixelGAN = Reconstruction + Adversarial Cost
Alireza Makhzani PixelGAN Autoencoders / 27 Outline 13 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors Clustering Semi-supervised Learning
Alireza Makhzani PixelGAN Autoencoders / 27 Global vs. Local Decomposition 14
Alireza Makhzani PixelGAN Autoencoders / 27 Code Space 15 Code Space of MNIST:
Alireza Makhzani PixelGAN Autoencoders / 27 Outline 16 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors ✦ Clustering ✦ Semi-supervised Learning
Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders with Categorical Priors 17
Alireza Makhzani PixelGAN Autoencoders / 27 Discrete vs. Continuous Decomposition (Clustering) 18
Alireza Makhzani PixelGAN Autoencoders / 27 Discrete vs. Continuous Decomposition (Clustering) 19 0.3% Error rate
Alireza Makhzani PixelGAN Autoencoders / 27 Unsupervised Clustering 20
Alireza Makhzani PixelGAN Autoencoders / 27 Unsupervised Clustering 21
Alireza Makhzani PixelGAN Autoencoders / 27 Unsupervised Clustering 22 5% Error rate
Alireza Makhzani PixelGAN Autoencoders / 27 Outline 23 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors ✦ Clustering ✦ Semi-supervised Learning
Alireza Makhzani PixelGAN Autoencoders / 27 Semi-supervised Learning 24 Semi-supervised Learning
Alireza Makhzani PixelGAN Autoencoders / 27 Semi-supervised Learning 25 Semi-supervised Learning
Alireza Makhzani PixelGAN Autoencoders / 27 Semi-supervised Classification 26
Alireza Makhzani PixelGAN Autoencoders / 2727 Thank you!

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PixelGAN Autoencoders

  • 1. Alireza Makhzani PixelGAN Autoencoders / 27 CIFAR Deep Learning Summer School Montreal, Canada Machine learning Group University of Toronto PixelGAN Autoencoders Alireza Makhzani, Brendan Frey June 29th, 2017 1
  • 2. Alireza Makhzani PixelGAN Autoencoders / 27 Outline 2 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors ✦ Clustering ✦ Semi-supervised Learning
  • 3. Alireza Makhzani PixelGAN Autoencoders / 27 PixelCNNs 33 ✦ Learn the image statistics directly at the pixel level. ✦ Good at modelling low-level pixel statistics. ✦ Samples lack global structure. ✦ Lacking latent representation. ✦ Conditional PixelCNNs can learn conditional densities. van den Oord et al., 2016
  • 4. Alireza Makhzani PixelGAN Autoencoders / 27 Variational Autoencoders 4 log p(x) > Eq(z|x)[ log(p(x|z)] KL(q(z|x)kp(z)) 4 Kingma et al., 2013 ✦ Good at capturing the global structure, but samples are blurry. ✦ Learn hierarchical representations useful for down-stream tasks. ✦ Attempts at combining PixelCNN with VAEs: • PixelVAE (Gulrajani et al., 2016) • VLAE (Chen et al., 2017)
  • 5. Alireza Makhzani PixelGAN Autoencoders / 27 Adversarial Autoencoders 5 A B C D Code Space of MNIST: Gaussian Prior Mixture of Gaussians Makhzani et al., 2015
  • 6. Alireza Makhzani PixelGAN Autoencoders / 27 Outline 6 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors ✦ Clustering ✦ Semi-supervised Learning
  • 7. Alireza Makhzani PixelGAN Autoencoders / 27 Limitations of Variational/Adversarial Autoencoders 7 ✦ All the image statistics are captured by the single latent vector. label, style global and local VAE None z x p(z) p(x|z) Latent Variable Deterministic (factorized Gaussians)
  • 8. Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 8 ✦ The image statistics are captured jointly by the latent vector and the autoregressive decoder. PixelCNN Latent Variablez x p(z) p(x|z)
  • 9. Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 9 PixelCNN Latent Variablez x p(z) p(x|z) GAN ✦ The image statistics are captured jointly by the latent vector and the autoregressive decoder.
  • 10. Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 10 PixelGAN (Gaussian) Global (low-frequency) Local (high-frequency) PixelGAN (Categorical) Discrete (label) Continuous (Style) PixelCNN Latent Variablez x p(z) p(x|z) GAN ✦ The image statistics are captured jointly by the latent vector and the autoregressive decoder.
  • 11. Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 11 Semi-supervised Learning PixelCNN Latent Variablez x p(z) p(x|z) GAN PixelGAN (Gaussian) Global (low-frequency) Local (high-frequency) PixelGAN (Categorical) Discrete (label) Continuous (Style) ✦ The image statistics are captured jointly by the latent vector and the autoregressive decoder.
  • 12. Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders 12 Cost function of PixelGAN = Reconstruction + Adversarial Cost
  • 13. Alireza Makhzani PixelGAN Autoencoders / 27 Outline 13 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors Clustering Semi-supervised Learning
  • 14. Alireza Makhzani PixelGAN Autoencoders / 27 Global vs. Local Decomposition 14
  • 15. Alireza Makhzani PixelGAN Autoencoders / 27 Code Space 15 Code Space of MNIST:
  • 16. Alireza Makhzani PixelGAN Autoencoders / 27 Outline 16 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors ✦ Clustering ✦ Semi-supervised Learning
  • 17. Alireza Makhzani PixelGAN Autoencoders / 27 PixelGAN Autoencoders with Categorical Priors 17
  • 18. Alireza Makhzani PixelGAN Autoencoders / 27 Discrete vs. Continuous Decomposition (Clustering) 18
  • 19. Alireza Makhzani PixelGAN Autoencoders / 27 Discrete vs. Continuous Decomposition (Clustering) 19 0.3% Error rate
  • 20. Alireza Makhzani PixelGAN Autoencoders / 27 Unsupervised Clustering 20
  • 21. Alireza Makhzani PixelGAN Autoencoders / 27 Unsupervised Clustering 21
  • 22. Alireza Makhzani PixelGAN Autoencoders / 27 Unsupervised Clustering 22 5% Error rate
  • 23. Alireza Makhzani PixelGAN Autoencoders / 27 Outline 23 1. Background • PixelCNNs • Variational Autoencoders • Adversarial Autoencoders 2. PixelGAN Autoencoders • Gaussian Priors • Categorical Priors ✦ Clustering ✦ Semi-supervised Learning
  • 24. Alireza Makhzani PixelGAN Autoencoders / 27 Semi-supervised Learning 24 Semi-supervised Learning
  • 25. Alireza Makhzani PixelGAN Autoencoders / 27 Semi-supervised Learning 25 Semi-supervised Learning
  • 26. Alireza Makhzani PixelGAN Autoencoders / 27 Semi-supervised Classification 26
  • 27. Alireza Makhzani PixelGAN Autoencoders / 2727 Thank you!