Introduction to Generative Adversarial Networks (GANs) and Their Applications

Every morning, scrolling through social media notifications is as routine as brushing my teeth. Like many, I’m also guilty of touching up my photos before sharing them—smoothing wrinkles and enhancing colors have become essential for online sharing.

But have you ever stopped to wonder how these editing tools work? 

It all comes down to something called generative adversarial networks, or GANs for short. These clever algorithms are the backbone of many image-editing applications, allowing users to alter their photos with ease.

Thousands of companies worldwide leverage GANs for a wide range of applications. So, let's take a closer look at how the GAN algorithm functions. 

What is GAN? 

The Generative Adversarial Network (GAN) is an unsupervised deep learning architecture where two neural networks compete to generate realistic new data from a given training dataset. One network, called the generator, creates new data samples from the input data and modifies them to be as authentic as possible. 

Meanwhile, the other network, known as the discriminator, attempts to distinguish between the generated data and real data from the original dataset. This adversarial setup leads to the continual improvement of the generator's ability to produce convincing fake data, as it learns to generate samples that are increasingly difficult for the discriminator to differentiate from real ones. 

The training process continues until the discriminator can no longer accurately determine whether the generated data is real or fake, resulting in the generation of high-quality synthetic data.

Components of Generative Adversarial Networks 

GAN architecture comprises two parts - the generator and the discriminator. In simple words, the generator acts like a thief and generates fake samples based on original data. Whereas, the discriminator acts as the police whose role is to identify the abnormalities in the generated sample and classify it as fake or real.  

Generator 

This component is a part of the GAN structure which learns to create fake data on receiving feedback from the discriminator. The generator learns to make the discriminator characterize its output as real. 

Compared to discriminator training, GAN AI generator training needs tighter integration among the two components. The generator takes random noise as input and transforms it into a meaningful output. 

Discriminator

A discriminator in a GAN neural network acts as a classifier. It attempts to distinguish between the fake data generated from the real data. 

The training data of the discriminator comes from two sources: 

• Fake data instances are produced by the generator. These are used as negative examples during discriminator training. 

• Real data instances like real images of people. These are used as positive examples during discriminator training. 
  

How does the generative adversarial network work? 

The two neural networks of GAN train in an adversarial manner. One network tries to generate new data, and the other tries to predict if the output is fake or real. 

A simplistic overview of the entire computing process is as follows: 

1. The generator analyzes the input training set and identifies the data attributes. 

2. On the other hand, the discriminator neural network also tries to analyze the initial training data and distinguishes between the characteristics independently. 

3. The generator modifies some attributes of the data by adding noise (or random changes) to some attributes. 

4. This modified fake data is passed to the discriminator. 

5. The discriminator analyzes the modified data to find the probability that the generated output is a part of the original dataset. 

6. The generator is offered guidance by the discriminator to reduce the noise vector randomization in the upcoming cycle. 

The generator tries to maximize the mistake probability by the discriminator, however, the discriminator tries to minimize the error probability. 

During training, both the generator and discriminator neural networks evolve and confront each other constantly until an equilibrium state is reached. In this state, the discriminator will no longer be able to recognize synthesized data. At this point, the GAN machine learning model training is over. 

Types of generative adversarial networks 

Several forms of GANs can be used for various tasks. Here are some of the most important types:

• Vanilla GAN: This basic GAN model comprises a generator and discriminator neural network. The generator creates data variations independently of the discriminator's feedback. Through stochastic gradient descent, the algorithm optimizes the equation, processing one example at a time from the dataset. 

Images are generated and classified using the neural networks as simple multi-layer perceptrons. The generator captures data distribution, while the discriminator assesses the likelihood of input data belonging to a specific class.

• Conditional generative adversarial network: This neural architecture introduces the concept of conditionality, allowing for target data generation. The generator and the conditioner are provided with additional information, in the form of class labels or some other kind of conditioning data. 

Let’s say, you want to generate the image of a cat. The conditions that you provide can have a label describing the image content, like cat images with the label ‘cat’. Conditioning helps the generator produce data that meets specific conditions. 

• Deep convolutional GAN: Using the power of convolutional neural networks (CNNs) in image processing, the CNN architecture is integrated into the deep convolutional GAN. High-resolution images are generated in this network that can be differentiated. 

The generator in this network uses transported convolutions to upscale data distribution, whereas the discriminator uses convolutional layers for classifying data. It also introduces architectural guidelines that make training more stable. 

• Cycle GAN: One of the most common GAN architecture, it is generally used to learn to transform between images of various styles. 

This is a fun neural network to experiment with. If I want to alter an image, I can train this algorithm to do so. Do you want to change the background of your image and add a scenic backdrop? This algorithm will help you do that. 

• Super-resolution GAN 

The primary goal of this network is to increase the resolution of an image. However, the image quality and details are maintained, when enhancing the image resolution. 

• Laplacian Pyramid GAN 

This type of GAN network addresses the challenge of generating high-resolution images by dividing the problem into stages. It employs a hierarchical approach, with several discriminators and generations working at different image resolutions. 

The process starts by generating a low-resolution image which improves in quality with progressive GAN stages. 

Generative Adversarial Networks Applications 

GANs have many applications in today's world. Here are some uses of generative adversarial networks: 

• Generating images: GANs image generation is used widely in many fields. They can create realistic images from text-based prompts by modifying existing images. A GANs image can help in creating immersive and realistic visual experiences in digital entertainment and video games. 

• Security: Virtual attackers are developing sophisticated software and are using social engineering to target individuals and organizations to steal sensitive information. 

Modern GAN can mask medical images, employee images, or street-view images rendering them useless for the attacker. 

• Data augmentation: It is used for artificially increasing the training set by creating modified copies of data with existing data. 

Generative models can be used for data augmentation to generate synthetic data having all the attributes of real-world data. 

• Fill in missing information: Sometimes the generative model is used for accurately guessing and completing missing information in a dataset. 

For example, GAN can be trained to generate images of the surface below the ground. For this, it needs to understand the correlation between the underground structures and the surface data. By studying the available sub-surface images, GAN creates new ones using terrain maps. 

Advantages of Generative Adversarial Networks AI

These neural networks have several advantages. Some of these benefits have been listed below: 

• High-quality results: These neural networks can produce high-quality results during image synthesis, music synthesis, and video synthesis. 

• Synthetic data generation: The generator in GAN can produce new synthetic data for training the discriminator. It is useful in data augmentation, creative analysis, and anomaly detection. 

• Versatility: GANs have various uses such as text-to-image synthesis, image synthesis, image-to-text translation, data augmentation, anomaly detection, etc. 

• Unsupervised learning: These neural networks can be trained without any labeled data, making them ideal for unsupervised learning tasks. This is especially helpful when labeled data is scarce or difficult to find.
  

Disadvantages of Generative Adversarial Networks 

Even though there are many benefits of GANs, there are some disadvantages to it. These limitations have been listed below: 

• Computational cost: GANs require a lot of computational resources. Training GANs is a slow process, especially for large datasets or high-resolution images. 

• Training instability: Training GAN machine learning models can be challenging having a high risk of mode collapse, instability, or failure to converge. 

• Overfitting: These networks can overfit training data, to produce synthetic data which is too similar to the training data. This leads to a lack of diversity. 

• Interpretability and accountability: GAN in deep learning can be difficult to understand or explain, making it a challenge to ensure transparency, fairness, or accountability in the applications. 
  

How GAN is Used in NLP

Natural language processing (NLP) is a subset of artificial intelligence that understands, generates, and manipulates human language. There are many applications for it such as text summarization, chatbots, machine translation, sentiment analysis, etc. 

But how is GAN used in NLP? 

It is used for performing tasks such as paraphrasing, text generation, data augmentation, and style transfer. 

Applying GAN in NLP poses challenges. The generator struggles to provide smooth gradients for the discriminator's evaluation, and the discriminator can't offer meaningful feedback for the generator's improvement. To address this, various techniques have emerged, such as using discrete latent variables, reinforcement learning, or leveraging pre-trained language models.

Final Words 

AI is the future of computing, altering the way we live and think. Generative adversarial networks (GANs) form a powerful paradigm in the field of machine learning. This deep learning model can generate new synthetic data from scratch of information. 

However, there are not many people who have a proper understanding of this new technology. If you want to make a career in this field, check out the certified courses offered by upGrad. Their courses are designed by industry experts and can help you not only get a firm grasp of AI concepts but will upskill you enough to get lucrative job offers. 

Frequently Asked Questions  

1. What is a generative adversarial network?

It is a machine learning (ML) model comprising two neural networks - the generator and the discriminator. 

2. What is the purpose of GAN?

GANs are used for creating new data instances resembling the training data. The generator produces fake instances and the discriminator distinguishes between the real and fake data. 

3. What is CNN and GAN?

Both CNN and GAN are deep learning neural architectures. CNNs are mainly used for recognition and classification. Whereas, GANs are generative models generating new examples from a giant training set. 

4. What is an example of a GAN?

Generative adversarial networks are used for generating videos and images, enhancing data, transferring styles, and the like are just some examples of GAN. 

5. What are the components of GANs?

The two major components of GANs are the generator and the discriminator. 

6. What are the advantages of the GAN algorithm?

GANs are used for generating diverse data samples, that are helpful for training machine learning models. 

7. Are GANs supervised or unsupervised?

GAN is a unsupervised deep learning algorithm. 

8. How many layers are there in GAN?

GAN comprises of a sequential model with batch normalization. Tanh, convolutional, linear, reshaping and unsampling layers. 

9. Is GAN used in NLP?

GAN is used for performing various NLP tasks like paraphrasing, text generation, data augmentation, and style transfer. 

10. What are the limitations of GANs?

A key limitation is GANs is when the generator produces a limited range of samples.