Do Generative Adversarial Networks (GANs) Aid in Training AI models?

AI models that try to simulate human intelligence rely heavily on continuous training and new data to prevent the quality of the output from deteriorating. Human intelligence is a complex matter to understand because it’s unpredictable, constantly evolving, adapting, and involves creative thinking – just to mention a few of its characteristics. AI models must therefore evolve fast to simulate human intelligence for which it needs data and validation of its outputs. Generative Adversarial Networks (GANs) train AI models through an adversarial method which will be described later in this article to enable the models to constantly evolve and become better. GANs are widely used in the AI domain and have many use cases. Many experts mention that GANs are effective in training AI models but there are practical limitations as well.

What are Generative Adversarial Networks (GANs)?

Let’s try to understand the concept of Generative Adversarial Networks (GANs) with a simple example.

You are learning to draw landscapes under the guidance of an art teacher who is also learning to draw but knows more than you. You create an artwork and after reviewing it, the teacher provides some critical feedback. You go back to your drawing and improve it accordingly. While you have been working with your teacher, your teacher has also been learning about reviewing artwork, drawings, and other related stuff. You improve your drawing and the teacher, based on increased learning, gives more feedback. The process continues until the teacher believes that the drawing is good enough.

That is exactly how Generative Adversarial Networks (GANs) work. Generative Adversarial Networks (GANs) have two components: a generator and a discriminator. Think of the generator as the art student and the discriminator as the art teacher. The generator takes data and produces an output, for example, the image of a mountain. The discriminator reviews the data and sends critical feedback. However, the discriminator also reviews the output based on limited data that it has acquired. It matches the output with the real output and provides its feedback. In the process, the discriminator learns a lot about real-world data and the generator learns a lot from the feedback. The process goes on until a high-quality output is produced. You can think of the generator and the discriminator as adversaries that compete with each other to improve their respective outputs. In the process, a better output is produced.

How can GANs train AI models?

GANs execute a standard process or flow to train the AI models. The flow is described below.

• Initialization of the generator and discriminator

Initialization means starting up both the generator and the discriminator with random values or parameters that constitute the output. For example, the output may be an image and the parameters may be the pixel count, RGB values of the colors, and some image pattern data. Data is chosen randomly to avoid the chances of any bias.

• Generator training

The generator accepts random noise as data or inputs and generates the synthetic data samples as output. Some training may already have the original output to test whether the generator can generate an output that is close to the original.

• Discriminator training

The discriminator accepts both the synthetic data output that the generator produces and the real data so it can match both to find discrepancies, if any. The discriminator is trained to find discrepancies but in its early stages, it’s natural to expect it to not detect mismatches as well as is expected. However, generators evolve fast based on their learnings from the data it consumes regularly, and with time, it’s able to evaluate synthetic data better.

• Adversarial feedback loop

An adversarial feedback loop means both the generator and the discriminator learn from each other and improve. The generator continuously improves by implementing the feedback from the discriminator while the discriminator continuously improves by evaluating the synthetic data output the generator produces.

• Convergence

This is the final stage of the flow. Convergence means that the generator can now produce synthetic data output that is the same as the real data output. The discriminator, after rigorous training, will be unable to detect any mismatch between the real and the synthetic data output.

Case study

NVIDIA Research’s AI model has made a significant breakthrough in the niche of GANs. It works faster and more accurately than most other GANs that are used in the industry. The AI model can recreate the artwork of famous painters and can generate images of cancerous cells with a great deal of accuracy. The name of the AI model is NVIDIA StyleGAN2 and it uses a breakthrough neural training technique to generate artwork from the Metropolitan Museum of Art that looks original. This shows that the GANs have been evolving fast and it can train AI models to produce synthetic data that is exactly like the real.

Limitations

GANs have some significant limitations when it comes to dependencies for optimum performance.

• When you feed large volumes of data to both the generator and discriminator, they tend to compete with each other as adversaries. While this is a necessary condition for AI model training, the volume of data and the processing required can exert huge pressure on machines that can lead to failure.
• GANs are an expensive proposition because of the recurring infrastructural and data cost. Acquiring data is costly because of proprietary, legal, and ethical issues. Organizations have in the past struggled to obtain data.
• The cost factor makes GANs inaccessible to relatively smaller organizations with budget constraints. That can result in insufficient development of GAN technology.

Conclusion

Generative Adversarial Networks (GANs) are definitely an exciting proposition and it has been rapidly advancing. It has multiple use cases and can be incredibly helpful, especially in the medical sector where it can aid in recreating images and data for medical research. However, the cost of maintenance, infrastructural problems, and the huge issues with data acquisition have been proving a hindrance. There needs to be a consolidated and cohesive framework of data acquisition that commits to the ethical use of data and takes into account the cost of acquisition as well. This is important for the smaller organizations that are trying to enter the niche of GANs.