Today we are going to talk about an amazing deep-learning architecture that will help us understand how generating new data from given datasets actually works.

We are going to understand how this fascinating generative network actually works and we will understand how this can be utilised to create literally anything you want.

That is exactly right because you can virtually create images as well as generate videos and even 3D models with the help of Generative Adversarial Networks (GANs).

Apart from that the list of applications go way beyond just general image generation because GANs can be excellent when comes to generating new human faces as well as simulating things like ageing.

You can also use GANs for things like computer vision as well as generating frames between videos and that is a dream come true for video production companies because you can virtually turn 30fps videos into 60fps without additional data.

The possibilities are endless when it comes to using a technology like this for content creators and so much more including in industries as vital as healthcare.

So, let us start this blog by understanding what are GANs.

What Is a Generative Adversarial Network (GAN)?

GAN is a very powerful class of deep learning architecture where two neural networks are in constant competition with one another and are part of the process of unsupervised learning when it comes to Machine Learning.

There are primarily two elements in the form of two networks in a single GAN, it is the Generator which is a convolution neural network and the Discriminator which is a deconvolutional neural network.

The purpose of the Generator is to create realistic artificial data from existing data sets and the purpose of the Discriminator is to find out if that generated data is real or if it is created by the Generator.

These two neural networks are pitted against each other and tasked through adversarial training where the purpose of the Generator is to keep on producing even more accurate realistic data with each iteration and the Discriminator has to achieve the goal of real data from generated data every time.

This ‘competition’ of sorts goes on continuously unsupervised as a zero-sum game with one network’s win being another network’s loss.


This continuous competition helps the Generator get closer to producing realistic outputs every time and the Discriminator also gets worse at predicting if it is real or fake every time.


The input of the Discriminator is connected with the output of the Generator and this happens through backpropagation.


Finally, there comes a time when the Discriminator fails to recognise a generated image from a real image because the Generator gets trained and accurate to such an extent that it is able to produce near-realistic outcomes.

GANs Are Effective for A Particular Reason

The reason why GANs are so effective is simply because of Adversarial Training because they are run unsupervised and it is sometimes referred to as a cat and mouse game.

We must understand that the only outcome of this zero-sum game of training is for the Generator to become so accurate that the Discriminator keeps on losing after a certain period of time.

This exercise utilises a loss function where the Generator always tries to minimise the probability of the Discriminator finding out if the data is real or fake.


This means the Generator is not just trying to generate the most realistic data possible but it is doing it in a way where it is actively trying to fool the Discriminator.

This ultimately leads to the situation where the Discriminator is just unable to distinguish between real and fake samples.

GAN Working Process

The Start of The Process

The GAN working process starts when two neural networks are provided with different tasks where the Generator is tasked with creating new images from existing data.

The Discriminator is tasked with distinguishing by identifying real data from a particular data set from data created by the Generator.

The Generator Starts

The process begins when the generator utilises random noise vectors as inputs with random values and this is just the starting point of the reciprocating process.

This helps the Generator create new data samples and we are talking about things like images and even text.

The first outputs are not really very accurate and not really high quality and definitely not high quality or realistic enough to fool the Discriminator.

This works as a loss function with an equation like this.


The Discriminator Reciprocates

It is now the Discriminator’s turn as it is presented with two types of inputs, one of the inputs is real data from the actual training dataset and we know the other input is data generated by the Generator.

The output of that determines a score and if the score is 1 then that means that the Discriminator thinks the data is real and if the score is 0 then the Discriminator thinks that data is fake.

The accuracy of the Discriminator also improves with each cycle and the Discriminator learns to understand between real and fake data.

This is the equation for the loss function of the discriminator.


The Adversarial Training

Both the Generator and the Discriminator are awarded when they are able to make their goals complete with the goal of the Discriminator being to identify real or fake data and the goal of the Generator to fool the discriminator.

The reason why this process of machine learning is so effective is because you do not need any supervision and they continue working towards their own goals and they also continuously improve their accuracy.

The Adaptations

The adaptation and updates to the accuracy of both networks work in an incredible way because when that Discriminator is accurate, the Generator receives no reward.

And when the Generator is accurate, the discriminator receives a penalty because it was just fooled.

These Are the Advantages Of GAN

GANs Are Able to Produce Amazing Results

One of the most important advantages of GANs is that it is able to produce very high-quality results that are photo-realistic.

This means you can utilise this to create videos as well as music and images and definitely text of very high quality that resembles realistic results.


This is a prime example of GAN utilisation as you can see an image of a Viking Samurai eating rice with chopsticks. The person in the image does not exist and this never happened in reality.

GANs Do Not Require Any Supervision

One of the best things about GANs is that you do not need to operate them with supervision because it is unsupervised learning.

This can free up a lot of time for the ML engineer to focus on other models and not just keep their attention, focus and time on a single GAN model.

GANs Are Very Versatile

One of the reasons why GANs are preferred by multiple industries and suitable for multiple types of applications is because of the level of versatility you can expect with GANs.

You can accomplish a lot of tasks from things like text-to-image synthesis, image synthesis, image-to-image translation, data augmentation etc.

These Are Some of The Applications for GANs

Human Face Generation


One of the most impressive things GANs can do is generate pictures of human faces of people who do not exist.

This means GANs not only can produce new images of existing people but they can also produce images of people who do not actually exist in reality.


They can render completely new individuals and GANs are able to achieve photorealistic results that are very human-like.

Realistic Photograph Generation


GANs are able to produce realistic photographs of anything and everything which includes different scenes, different objects and even objects that do not exist in reality.


They are able to produce images of animals and humans as well as architecture and it all depends on the database they have been trained on.

Cartoon Character Generation

One of the most impressive utilisations of GANs is in the world of comics and cartoon characters because they are able to develop images of cartoon characters from existing image data sets.

However, the impressive thing is that they are able to produce entirely new cartoon characters and maintain those characters which in essence means you can utilise GANs to create entire new comics without drawing a single line.

Text To Image Translation

This is one of the most utilized applications of GANs where you are able to generate images based on text prompts.

This is one of the most useful applications of GANs that nearly every industry can utilise.

Photos To Emoji Generation

While this might not be as useful as the other options, this is just as impressive because GANs allow you to create custom emojis from photos.

This means you can create emojis that are accurate to yourself and it is actually quite fun to do.

Video Generation and Prediction

You can definitely use GANs for creating entire videos because the concept of creating videos is the same as the concept of creating images.

With videos, you just create new frames continuously and tie them together into a video.

GANs can also take existing videos and create entirely new sections out of it and this is something revolutionary and can be utilised not only by content creators but also for serious healthcare and forensic applications and much more.

3D Object Generation

3D object generation is one of the most popular utilizations of GANs by the gaming industry because video game developers can now simply utilise 2D images and create 3D objects from them.

This is actively used in the gaming industry for easy character generation and easy object generation such as creating the models of buildings and cars as well as landscapes.

This is just the tip of the iceberg when it comes to the utilisation of 3D objects because these objects generated by GANs can benefit nearly every kind of industry, even the manufacturing industry.

Video Resolution Improvement

Just like video creation and prediction, video resolution improvement can also be something easily accomplished with the help of trained GANs.

This is also a form of image prediction in that new pixels are simply predicted from existing datasets with very high-quality results.

Photo Resolution Improvement

GANs can be utilised to repair damaged parts of photos and also improve the resolution of existing photographs by simply filling in pixels of existing photographs.

This can be further transformed into anything you can imagine right from changing the clothes of a person in a photograph accurately to blending two or more photographs as well as achieving very high resolutions for the photographs.

Your imagination is the limit when it comes to GANs.

Video Frame Rate Improvement

You can utilise GANs to convert videos that are in a lower frame rate such as 30fps and basically convert that video into 60fps or even more if you want.

This is possible with the help of video prediction as just like video prediction where future frames of a video are predicted, predicting frames in between two frames is what helps GANs improve the frame rate of existing videos.

We hope this blog has helped you understand what are Generative Adversarial Networks (GANs) and the world of opportunity GANs can unlock for nearly every industry in the world.

GANs are working wonders when it comes to the field of generative modelling and the sky is the limit when it comes to a technology like this because GANs are just limited by your level of imagination on how you can utilise them.

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