InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsInfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial NetsChen, Xi and Chen, Xi and Duan, Yan and Houthooft, Rein and Schulman, John and Sutskever, Ilya and Abbeel, Pieter2016
Paper summaryaleju * Usually GANs transform a noise vector `z` into images. `z` might be sampled from a normal or uniform distribution.
* The effect of this is, that the components in `z` are deeply entangled.
* Changing single components has hardly any influence on the generated images. One has to change multiple components to affect the image.
* The components end up not being interpretable. Ideally one would like to have meaningful components, e.g. for human faces one that controls the hair length and a categorical one that controls the eye color.
* They suggest a change to GANs based on Mutual Information, which leads to interpretable components.
* E.g. for MNIST a component that controls the stroke thickness and a categorical component that controls the digit identity (1, 2, 3, ...).
* These components are learned in a (mostly) unsupervised fashion.
* The latent code `c`
* "Normal" GANs parameterize the generator as `G(z)`, i.e. G receives a noise vector and transforms it into an image.
* This is changed to `G(z, c)`, i.e. G now receives a noise vector `z` and a latent code `c` and transforms both into an image.
* `c` can contain multiple variables following different distributions, e.g. in MNIST a categorical variable for the digit identity and a gaussian one for the stroke thickness.
* Mutual Information
* If using a latent code via `G(z, c)`, nothing forces the generator to actually use `c`. It can easily ignore it and just deteriorate to `G(z)`.
* To prevent that, they force G to generate images `x` in a way that `c` must be recoverable. So, if you have an image `x` you must be able to reliable tell which latent code `c` it has, which means that G must use `c` in a meaningful way.
* This relationship can be expressed with mutual information, i.e. the mutual information between `x` and `c` must be high.
* The mutual information between two variables X and Y is defined as `I(X; Y) = entropy(X) - entropy(X|Y) = entropy(Y) - entropy(Y|X)`.
* If the mutual information between X and Y is high, then knowing Y helps you to decently predict the value of X (and the other way round).
* If the mutual information between X and Y is low, then knowing Y doesn't tell you much about the value of X (and the other way round).
* The new GAN loss becomes `old loss - lambda * I(G(z, c); c)`, i.e. the higher the mutual information, the lower the result of the loss function.
* Variational Mutual Information Maximization
* In order to minimize `I(G(z, c); c)`, one has to know the distribution `P(c|x)` (from image to latent code), which however is unknown.
* So instead they create `Q(c|x)`, which is an approximation of `P(c|x)`.
* `I(G(z, c); c)` is then computed using a lower bound maximization, similar to the one in variational autoencoders (called "Variational Information Maximization", hence the name "InfoGAN").
* Basic equation: `LowerBoundOfMutualInformation(G, Q) = E[log Q(c|x)] + H(c) <= I(G(z, c); c)`
* `c` is the latent code.
* `x` is the generated image.
* `H(c)` is the entropy of the latent codes (constant throughout the optimization).
* Optimization w.r.t. Q is done directly.
* Optimization w.r.t. G is done via the reparameterization trick.
* If `Q(c|x)` approximates `P(c|x)` *perfectly*, the lower bound becomes the mutual information ("the lower bound becomes tight").
* In practice, `Q(c|x)` is implemented as a neural network. Both Q and D have to process the generated images, which means that they can share many convolutional layers, significantly reducing the extra cost of training Q.
* They use for `c` one categorical variable (10 values) and two continuous ones (uniform between -1 and +1).
* InfoGAN learns to associate the categorical one with the digit identity and the continuous ones with rotation and width.
* Applying Q(c|x) to an image and then classifying only on the categorical variable (i.e. fully unsupervised) yields 95% accuracy.
* Sampling new images with exaggerated continuous variables in the range `[-2,+2]` yields sound images (i.e. the network generalizes well).
* ![MNIST examples](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/InfoGAN__mnist.png?raw=true "MNIST examples")
* 3D face images
* InfoGAN learns to represent the faces via pose, elevation, lighting.
* They used five uniform variables for `c`. (So two of them apparently weren't associated with anything sensible? They are not mentioned.)
* 3D chair images
* InfoGAN learns to represent the chairs via identity (categorical) and rotation or width (apparently they did two experiments).
* They used one categorical variable (four values) and one continuous variable (uniform `[-1, +1]`).
* InfoGAN learns to represent lighting and to spot the center digit.
* They used four categorical variables (10 values each) and two continuous variables (uniform `[-1, +1]`). (Again, a few variables were apparently not associated with anything sensible?)
* InfoGAN learns to represent pose, presence of sunglasses (not perfectly), hair style and emotion (in the sense of "smiling or not smiling").
* They used 10 categorical variables (10 values each). (Again, a few variables were apparently not associated with anything sensible?)
* ![CelebA examples](https://raw.githubusercontent.com/aleju/papers/master/neural-nets/images/InfoGAN__celeba.png?raw=true "CelebA examples")
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Neural Information Processing Systems Conference - 2016 via Local Bibsonomy