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Reading group gan - 20170417

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Introduction to generative adversarial networks

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Reading group gan - 20170417

  1. 1. Generative Adversarial Networks NIPS, Ian Goodfellow Presenter: Shuai Zhang, CSE, UNSW
  2. 2. Content Introduction Generative Model Framework of GANs How do GANs work Research frontiers
  3. 3. Introduction “There are many interesting recent development in deep learning…The most important one, in my opinion, is adversarial training (also called GAN for Generative Adversarial Networks). This, and the variations that are now being proposed is the most interesting idea in the last 10 years in ML, in my opinion.” – Yann LeCun
  4. 4. Generative Model generative models: any model that takes a training set, consisting of samples drawn from a distribution p_data, and learns to represent an estimate of that distribution somehow A discriminative model learns a function that maps the input data (x) to some desired output class label (y). In probabilistic terms, they directly learn the conditional distribution P(y|x). A generative model tries to learn the joint probability of the input data and labels simultaneously, i.e. P(x,y). This can be converted to P(y|x) for classification via Bayes rule, but the generative ability could be used for something else as well, such as creating likely new (x, y) samples.
  5. 5. Generative Model generative models: any model that takes a training set, consisting of samples drawn from a distribution p_data, and learns to represent an estimate of that distribution somehow
  6. 6. Generative Model Advantages • represent and manipulate high-dimensional probability distributions • incorporated into reinforcement learning • can be trained with missing data and can provide predictions on inputs that are missing data • enable machine learning to work with multi-modal outputs • many tasks intrinsically require realistic generation of samples from some distribution
  7. 7. Generative Model Example 1: generate images
  8. 8. Generative Model Example 2: Vector Space Arithmetic
  9. 9. Generative Model Example 3: super-resolution
  10. 10. Generative Model Example 4: Generative Image manipulation https://www.youtube.com/watch?v=9c4z6YsBGQ0
  11. 11. Generative Model Example 5: Image-to-image translation
  12. 12. Framework of GANs
  13. 13. Framework of GANs • Generator • One takes noise as input and generates sample • Discriminator • receives samples from both the generator and the training data, and has to be able to distinguish between the two sources • Relationship between these two neural network: Competing https://ishmaelbelghazi.github.io/ALI
  14. 14. How do GANs work Symbol Meaning D(x, 𝜃 𝑑) A differentiable function The probability that x came from the data rather than 𝑝 𝑔 𝜃 𝑑 Parameters of D 𝑝 𝑔 The generator distribution G(z; 𝜃𝑔) A differentiable function 𝜃𝑔 Parameters of G z Input noise 𝑃𝑧(z) A prior on input noise variables Notation
  15. 15. How do GANs work We train D to maximize the probability of assigning the correct label to both training examples and samples from G. We simultaneously train G to minimize log(1 − D(G(z))). Minimax Game:
  16. 16. How do GANs work Cost function for discriminator: 𝐽 𝐷 This is just the standard cross-entropy cost that minimized when training a standard binary classifier with a sigmoid output.
  17. 17. How do GANs work Cost function for generator: 𝐽 𝐺 Zero-Sum Game ( also called minimax game), in which the sum of all player’s costs is always zero. So:
  18. 18. How do GANs work Cost function for generator: 𝐽 𝐺 Disadvantages for this cost function: The generator’s gradient vanishes To solve this problem, we continue to use cross-entropy minimization for the generator. We just need to flip the sign of the cost function above, and we get:
  19. 19. How do GANs work
  20. 20. How do GANs work For each epoch you only need to do a two-step training.
  21. 21. How do GANs work For each epoch you only need to do a two-step training.
  22. 22. How do GANs work For each epoch, you only need to do a two-step training.
  23. 23. How do GANs work https://www.youtube.com/watch?v=0r3g7- 4bMYU&feature=youtu.be
  24. 24. Research frontiers • Non-convergence: Game solving algorithms may not approach an equilibrium at all • Mode Collapse: causes low output diversity • Evaluation: There is not any single compelling way to evaluate a generative model • Discrete outputs: G must be differentiable • Finding equilibria in games: Simultaneous SGD on two players costs may not converge to a Nash equilibrium
  25. 25. References 1. http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/Generative Model 2. http://www.iangoodfellow.com/slides/2016-12-04-NIPS.pdf 3. https://arxiv.org/pdf/1701.00160.pdf 4. https://zhuanlan.zhihu.com/p/25095905 5. http://gkalliatakis.com/blog/delving-deep-into-gans 6. http://www.rricard.me/machine/learning/generative/adversarial/networks/2017/04/05/gans-part1.html
  26. 26. Thanks! Q & A

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