In this blog, we are going to see the introduction to generative adversarial networks (GAN) and cGAN. A generative adversarial network is a class of machine learning frameworks for training generative models. Generative models create new data instances that resemble the training data.
Given a training set, a GAN learns to generate new data with the same statistics as the training set. GANs depend on the training loss of the model, the model tries to minimize loss to generate as real images as possible.
Table of content
- What is GAN and How does it work?
- What is Conditional GAN?
- Advantages of cGAN
- Pictorial explanation
- Use-cases
- What is GAN and How does it work?
GAN is a generative model which achieves a high level of realism by pairing a generator with a discriminator.
The generator learns to produce the target output, while the discriminator learns to distinguish true data from the output of the generator.
To give you an analogy, the generator tries to fool the discriminator, and the discriminator tries to keep from being fooled.
Initially, we train the discriminator with a known dataset. The training involves presenting the discriminator with samples from the training dataset until it achieves acceptable accuracy. Then, the generator trains based on if it is successful in fooling the discriminator. Typically, the generator is fed with randomized input that is sampled from a predefined latent space (e.g a multivariate normal distribution). Consequently, the output generated by the generator is evaluated by the discriminator.
- Generator network -Takes as input random vector, and decodes it into a synthetic image
- Discriminator network – Takes as input an image, and predicts whether the image came from the training set or was created by the generator network.
(Citation: Deep learning with python by Francois Chollet)
GANs rely on back-propagation on both networks to minimize the errors so that the generator produces better images, while the discriminator becomes most skilled at flagging synthetic images. Typically, the discriminator is a convolution neural network, and the generator is a deconvolutional neural network.
Although originally proposed as a form of a generative model for unsupervised learning, GANs have also proven useful for supervised learning and reinforcement learning.
- What is a Conditional GAN?
A conditional generative adversarial network, or cGAN for short, is a type of GAN that involves the conditional generation of images by the generator model.
In our previous section, we have explored GANs where we have no control over the type of output that is being produced. Unlike most generative network architectures, cGANs are not completely unsupervised in their training methods. These cGAN network architectures require some kind of class labels or labeled data to perform the desired action. Let us understand the difference between a simple GAN architecture and a cGAN architecture with some mathematical formulas. Firstly, let us explore the mathematical expression for the GAN structure, as shown below.
With a minor modification to our previous formula of the simple GAN architecture, we have now added a y-label to both the discriminator and the generator network. By converting the previous probabilities into conditional probabilities with the addition of the 'y'-labels, we can ensure that the training generator and discriminator networks are now trained only for the respective label. Hence, we can send a particular input label and receive the desired output from the generative network once the training procedure is complete.
Both the generator and discriminator networks will have these labels assigned to them during the training process. Hence, both these networks of the cGAN architecture are trained conditionally such that the generator generates only outputs similar to the expected label output, while the discriminator model ensures to check if the generated output is real or fake alongside checking if the image matches the particular label.
- Advantages of cGANs
By providing additional information to the model, we get two benefits:
- Convergence will be faster. Even the random distribution that the fake images follow will have some pattern.
- You can control the output of the generator at test time by giving the label for the image you want to generate.
- Explanation with picture
If that was confusing, consider this example for gaining more understanding:
Suppose you train a GAN on hand-written digits (MNIST dataset). You normally cannot control what specific images the generator will produce. In other words, there is no way you can request a particular digit image from the generator.
This is where the cGANs come in as we can add an additional input layer of one-hot-encoded image labels. This additional layer guides the generator in terms of which image to produce.
The input to the additional layer can be a feature vector derived from either an image that encodes the class or a set of specific characteristics we expect from the image.
Conditional generative adversarial networks are not strictly unsupervised learning algorithms because they require labeled data as input to the additional layer.
- Use cases of cGANs
- Image-to-image translation
- Text to image synthesis
- Video generation
- Convolutional face generation
