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Deep Image Generating Models
/
Imperial College London
2016-11-23
Kai Arulkumaran @KaiLashArul
Foreword
Deep learning is a great creative tool
We can generate novel media in unexpected ways
(e.g. DeepDream/Inceptionism [1])
We can remix media (e.g. style transfer [2])
We can directly use deep generative models
The following applies to more than just images
Summary
Generative adversarial networks (GANs) [3]
Variational autoencoders (VAEs) [4, 5]
Autoregressive networks [6-8]
Generation
Let's create an image using a starting value
Speci cally, some random noise,
maybe sampled from a Gaussian: z ∼  (0, 1)
Create a transformation model that takes
and returns an image
f z
x
Images from space are generated from a value ∼ P(Z)
Networks as Functions
Arti cial neural networks are powerful function approximators
Approximate (many) continuous functions in
(universal approximation theorem) [9]
ℝ
n
Learn network parameters, , to satisfy a criterionθ
Generator Function
Learn a generator function, , that creates images:G(z; θ) x = G(z)
What criterion to train ?G
Discriminator Function
Train a discriminator function, , to label images:D(x; ϕ) y = D(x)
Learn to distinguish real images: when
Learn to distinguish fake images: when
(y = 1) x ∼ p(X)
(y = 0) x = G(z)
Adjust to maximise both criterionsϕ
Minimax Game
Train using the minimax rule from game theory [3]G
[log(D(x))] + [1 − log(D(G(z)))]minθ maxϕ x∼p(X) z∼p(Z)
never sees real images, but learns to create images
that would fool
G
D
GANs turn density estimation into an easier problem - classi cation
DCGAN
Convolutional neural networks improve GAN capabilities [10]
GAN Generations
Preserve general image statistics, sharp edges
Fail to preserve spatial relationships/coherence
Interpolations
Take 2 samples, linearly spherically interpolate [11], generate
Image Arithmetic
Networks learn a manifold (locally Euclidean space)
Conditional GANs
Use information about class of object [12-15]
Inference
Impose more meaning on latent space
Observation is generated by a latent variablex z
Inference tries to retrieve which was responsible for whichz x
Probabilistically, generation is
and inference is
x ∼ P(x|z)
z ∼ P(z|x)
Autoencoders learn both together
for "true" distributions, for model distributionsP Q
Autoencoders
Neural network encoder, , with encodinge z = e(x)
Decoder, , with decodingd x = d(z)
learns , learnse Q(z|x; θ) d Q(x|z; θ)
Compose networks, , and train jointlyd ∘ e
Criterion is minimising distance between real input
and reconstruction
x
d(e(x))
Mean square error/cross entropy criterions correspond to
maximising likelihood of reconstruction
Generative Autoencoders
Constrain encodings to follow a prior probability distribution, P(Z)
Idea 1: Directly sample from stochastic neurons
Optimisation requires estimating gradient over expectation,
naively requiring (Monte Carlo) sampling
Idea 2: Reparameterise to a deterministic function + noise source [4]
Encoder outputs parameters for a probability distribution
Criterion penalises di erence between
desired distribution parameters and encoder outputs
Stochastic samples via the reparameterisation trick
Variational Autoencoders
VAEs are latent variable models trained with variational inference
Maximise variational/evidence lower bound
[log(p(x|z))] − [Q(Z|X)‖P(Z)]q(z|x) DKL
KL divergence penalises deviating fromQ(Z|X) P(Z)
Variational Bayes w/ mean- eld approximation reverse KL divergence⟹
Divergence Behaviours
Forward KL divergence, , is "zero-avoiding",
covering, ensures whenever
[P‖Q]DKL
q(z) > 0 p(z) > 0
Reverse KL divergence, , is "zero-forcing", nds modes[Q‖P]DKL
Jensen-Shannon divergence
= [P‖ ] + [Q‖ ]DJS
1
2
DKL
P+Q
2
1
2
DKL
P+Q
2
GANs minimise JS divergence assuming is Bayes optimalD
Discriminative Regularisation
Reconstruction (bottom) of real image (middle) is blurry
in uncertain regions (such as hair detail)
Discriminative loss using pretrained network (top) [16, 17]
MCMC Sampling
does not perfectly match ; resolve with sampling [18]Q(Z) P(Z)
Sequential Drawing
Paint on canvas using recurrent neural network [19]
DRAW: A Recurrent Neural Network For Image G...
Conditional on Text
Condition generation on a text caption [20]
Independence Assumption
So far, pixels were created independently of each other,
given the penultimate layer
Autoregressive networks generate pixels one at a time,
conditional on the previous [6-8]
Conclusion
Deep generative models have improved a lot in a few years
Images are intuitively interpretable for qualitative evaluation
Generative models are hard to evaluate quantitatively [21]
Potential uses, e.g. procedural content generation
For more depth, see Building Machines that Imagine and Reason
Figures
1.
2.
3.
4.
5.
6.
7.
8.
Google Research Blog: Inceptionism: Going Deeper into Neural Networks
Neural Networks, Manifolds, and Topology -- colah's blog
Newmu/dcgan_code - GitHub
Pattern Recognition and Machine Learning | Christopher Bishop | Springer
[1602.03220] Discriminative Regularization for Generative Models
[1610.09296] Improving Sampling from Generative Autoencoders with Markov Chains
DRAW: A Recurrent Neural Network For Image Generation by Google DeepMind - YouTube
[1511.02793] Generating Images from Captions with Attention
References
1. Mordvintsev, A., Olah, C., & Tyka, M. (2015). Inceptionism: Going deeper into neural networks. Google Research Blog.
2. Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576.
3. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in
Neural Information Processing Systems (pp. 2672-2680).
4. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
5. Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. arXiv
preprint arXiv:1401.4082.
6. Larochelle, H., & Murray, I. (2011). The Neural Autoregressive Distribution Estimator. In AISTATS (Vol. 1, p. 2).
7. Gregor, K., Danihelka, I., Mnih, A., Blundell, C., & Wierstra, D. (2013). Deep autoregressive networks. arXiv preprint arXiv:1310.8499.
8. van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel Recurrent Neural Networks. arXiv preprint arXiv:1601.06759.
9. Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural networks, 4(2), 251-257.
10. Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks.
arXiv preprint arXiv:1511.06434.
11. White, T. (2016). Sampling Generative Networks: Notes on a Few E ective Techniques. arXiv preprint arXiv:1609.04468.
12. Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784.
13. Odena, A. (2016). Semi-Supervised Learning with Generative Adversarial Networks. arXiv preprint arXiv:1606.01583.
14. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. arXiv preprint
arXiv:1606.03498.
15. Odena, A., Olah, C., & Shlens, J. (2016). Conditional Image Synthesis With Auxiliary Classi er GANs. arXiv preprint arXiv:1610.09585.
16. Dosovitskiy, A., & Brox, T. (2016). Generating images with perceptual similarity metrics based on deep networks. arXiv preprint
arXiv:1602.02644.
17. Lamb, A., Dumoulin, V., & Courville, A. (2016). Discriminative Regularization for Generative Models. arXiv preprint arXiv:1602.03220.
18. Arulkumaran, K., Creswell, A., & Bharath, A. A. (2016). Improving Sampling from Generative Autoencoders with Markov Chains. arXiv preprint
arXiv:1610.09296.
19. Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., & Wierstra, D. (2015). DRAW: A recurrent neural network for image generation. arXiv
preprint arXiv:1502.04623.
20. Mansimov, E., Parisotto, E., Ba, J. L., & Salakhutdinov, R. (2015). Generating images from captions with attention. arXiv preprint
arXiv:1511.02793.
21. Theis, L., Oord, A. V. D., & Bethge, M. (2015). A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844.
Thanks
Friends on Twitter for posts and discussions
Toni Creswell, equal contributor on [16]
Colleagues at BICV and Computational Neurodynamics

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Deep image generating models

  • 2. Foreword Deep learning is a great creative tool We can generate novel media in unexpected ways (e.g. DeepDream/Inceptionism [1]) We can remix media (e.g. style transfer [2]) We can directly use deep generative models The following applies to more than just images
  • 3. Summary Generative adversarial networks (GANs) [3] Variational autoencoders (VAEs) [4, 5] Autoregressive networks [6-8]
  • 4. Generation Let's create an image using a starting value Speci cally, some random noise, maybe sampled from a Gaussian: z ∼  (0, 1) Create a transformation model that takes and returns an image f z x Images from space are generated from a value ∼ P(Z)
  • 5. Networks as Functions Arti cial neural networks are powerful function approximators Approximate (many) continuous functions in (universal approximation theorem) [9] ℝ n Learn network parameters, , to satisfy a criterionθ
  • 6. Generator Function Learn a generator function, , that creates images:G(z; θ) x = G(z) What criterion to train ?G
  • 7. Discriminator Function Train a discriminator function, , to label images:D(x; ϕ) y = D(x) Learn to distinguish real images: when Learn to distinguish fake images: when (y = 1) x ∼ p(X) (y = 0) x = G(z) Adjust to maximise both criterionsϕ
  • 8. Minimax Game Train using the minimax rule from game theory [3]G [log(D(x))] + [1 − log(D(G(z)))]minθ maxϕ x∼p(X) z∼p(Z) never sees real images, but learns to create images that would fool G D GANs turn density estimation into an easier problem - classi cation
  • 9. DCGAN Convolutional neural networks improve GAN capabilities [10]
  • 10. GAN Generations Preserve general image statistics, sharp edges Fail to preserve spatial relationships/coherence
  • 11. Interpolations Take 2 samples, linearly spherically interpolate [11], generate
  • 12. Image Arithmetic Networks learn a manifold (locally Euclidean space)
  • 13. Conditional GANs Use information about class of object [12-15]
  • 14. Inference Impose more meaning on latent space Observation is generated by a latent variablex z Inference tries to retrieve which was responsible for whichz x Probabilistically, generation is and inference is x ∼ P(x|z) z ∼ P(z|x) Autoencoders learn both together for "true" distributions, for model distributionsP Q
  • 15. Autoencoders Neural network encoder, , with encodinge z = e(x) Decoder, , with decodingd x = d(z) learns , learnse Q(z|x; θ) d Q(x|z; θ) Compose networks, , and train jointlyd ∘ e Criterion is minimising distance between real input and reconstruction x d(e(x)) Mean square error/cross entropy criterions correspond to maximising likelihood of reconstruction
  • 16. Generative Autoencoders Constrain encodings to follow a prior probability distribution, P(Z) Idea 1: Directly sample from stochastic neurons Optimisation requires estimating gradient over expectation, naively requiring (Monte Carlo) sampling Idea 2: Reparameterise to a deterministic function + noise source [4] Encoder outputs parameters for a probability distribution Criterion penalises di erence between desired distribution parameters and encoder outputs Stochastic samples via the reparameterisation trick
  • 17. Variational Autoencoders VAEs are latent variable models trained with variational inference Maximise variational/evidence lower bound [log(p(x|z))] − [Q(Z|X)‖P(Z)]q(z|x) DKL KL divergence penalises deviating fromQ(Z|X) P(Z) Variational Bayes w/ mean- eld approximation reverse KL divergence⟹
  • 18. Divergence Behaviours Forward KL divergence, , is "zero-avoiding", covering, ensures whenever [P‖Q]DKL q(z) > 0 p(z) > 0 Reverse KL divergence, , is "zero-forcing", nds modes[Q‖P]DKL Jensen-Shannon divergence = [P‖ ] + [Q‖ ]DJS 1 2 DKL P+Q 2 1 2 DKL P+Q 2 GANs minimise JS divergence assuming is Bayes optimalD
  • 19. Discriminative Regularisation Reconstruction (bottom) of real image (middle) is blurry in uncertain regions (such as hair detail) Discriminative loss using pretrained network (top) [16, 17]
  • 20. MCMC Sampling does not perfectly match ; resolve with sampling [18]Q(Z) P(Z)
  • 21. Sequential Drawing Paint on canvas using recurrent neural network [19] DRAW: A Recurrent Neural Network For Image G...
  • 23. Independence Assumption So far, pixels were created independently of each other, given the penultimate layer Autoregressive networks generate pixels one at a time, conditional on the previous [6-8]
  • 24. Conclusion Deep generative models have improved a lot in a few years Images are intuitively interpretable for qualitative evaluation Generative models are hard to evaluate quantitatively [21] Potential uses, e.g. procedural content generation For more depth, see Building Machines that Imagine and Reason
  • 25. Figures 1. 2. 3. 4. 5. 6. 7. 8. Google Research Blog: Inceptionism: Going Deeper into Neural Networks Neural Networks, Manifolds, and Topology -- colah's blog Newmu/dcgan_code - GitHub Pattern Recognition and Machine Learning | Christopher Bishop | Springer [1602.03220] Discriminative Regularization for Generative Models [1610.09296] Improving Sampling from Generative Autoencoders with Markov Chains DRAW: A Recurrent Neural Network For Image Generation by Google DeepMind - YouTube [1511.02793] Generating Images from Captions with Attention
  • 26. References 1. Mordvintsev, A., Olah, C., & Tyka, M. (2015). Inceptionism: Going deeper into neural networks. Google Research Blog. 2. Gatys, L. A., Ecker, A. S., & Bethge, M. (2015). A neural algorithm of artistic style. arXiv preprint arXiv:1508.06576. 3. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (pp. 2672-2680). 4. Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114. 5. Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082. 6. Larochelle, H., & Murray, I. (2011). The Neural Autoregressive Distribution Estimator. In AISTATS (Vol. 1, p. 2). 7. Gregor, K., Danihelka, I., Mnih, A., Blundell, C., & Wierstra, D. (2013). Deep autoregressive networks. arXiv preprint arXiv:1310.8499. 8. van den Oord, A., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel Recurrent Neural Networks. arXiv preprint arXiv:1601.06759. 9. Hornik, K. (1991). Approximation capabilities of multilayer feedforward networks. Neural networks, 4(2), 251-257. 10. Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434. 11. White, T. (2016). Sampling Generative Networks: Notes on a Few E ective Techniques. arXiv preprint arXiv:1609.04468. 12. Mirza, M., & Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784. 13. Odena, A. (2016). Semi-Supervised Learning with Generative Adversarial Networks. arXiv preprint arXiv:1606.01583. 14. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. arXiv preprint arXiv:1606.03498. 15. Odena, A., Olah, C., & Shlens, J. (2016). Conditional Image Synthesis With Auxiliary Classi er GANs. arXiv preprint arXiv:1610.09585. 16. Dosovitskiy, A., & Brox, T. (2016). Generating images with perceptual similarity metrics based on deep networks. arXiv preprint arXiv:1602.02644. 17. Lamb, A., Dumoulin, V., & Courville, A. (2016). Discriminative Regularization for Generative Models. arXiv preprint arXiv:1602.03220. 18. Arulkumaran, K., Creswell, A., & Bharath, A. A. (2016). Improving Sampling from Generative Autoencoders with Markov Chains. arXiv preprint arXiv:1610.09296. 19. Gregor, K., Danihelka, I., Graves, A., Rezende, D. J., & Wierstra, D. (2015). DRAW: A recurrent neural network for image generation. arXiv preprint arXiv:1502.04623. 20. Mansimov, E., Parisotto, E., Ba, J. L., & Salakhutdinov, R. (2015). Generating images from captions with attention. arXiv preprint arXiv:1511.02793. 21. Theis, L., Oord, A. V. D., & Bethge, M. (2015). A note on the evaluation of generative models. arXiv preprint arXiv:1511.01844.
  • 27. Thanks Friends on Twitter for posts and discussions Toni Creswell, equal contributor on [16] Colleagues at BICV and Computational Neurodynamics