What is so special about Generative Adversarial Network (GAN)

Fans are ecstatic for a variety of reasons, including the fact that GANs were the first generative algorithms to produce convincingly good results, as well as the fact that they have opened up many new research directions. In the last several years, GANs are considered to be the most prominent machine learning research, and since then, GANs have revolutionized the deep learning concepts, which has produced some major technological breakthroughs in artificial intelligence history

Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a powerful class of neural networks that are used for unsupervised learning. It is basically made up of a system of two competing neural network models which compete with each other and are able to analyze, capture and copy the variations within a dataset.

Feature of Generative Adversarial Network

1. The first and greatest feature about GANs is their learning nature, which is that they prefer to follow powerful unsupervised learning, which is why they don’t require labelled data. This makes GANs highly powerful and easy to grasp because the tedious task of labelling and annotating data is eliminated.
2. Second, they suggested a generative model that, when coupled with an adversarial network, can create high-quality natural pictures that grow progressively to generate more and more realistic-looking data. This framework can not only produce very high-quality synthetic data, but it can also be used to improve pixels in photographs, generate images from the input text, convert images from one domain to another, modify the appearance of the facial image, and many other things.
3. Third, suppose you don’t have enough data for a problem you’re working on; in that case, you can use adversarial networks to “generate” more data instead of resorting to tricks like data augmentation; not only that, many tasks require the realistic generation of samples from some distribution, and GANs have proven to be very useful in such cases.
4. When we have several tasks to complete, a single input may have a close relation to many alternative right outputs, each of which is acceptable. Most Generative models, and GANs, in particular, allow machine learning to operate with multi-modal outputs.
5. Another reason for GANs’ popularity is the strength of adversarial training, which produces considerably sharper and discrete outputs than MSE’s hazy averages. This has led to multiple applications of GANs, including super-resolution GANs, which outperform MSE and several other loss functions in trend. 
6. Last but not least, the never-ending research centered on GANs is so captivating that it has attracted the interest of every other industry. As a result, we shall witness several key technological advancements in the history of GANs that have elevated them to prominence.

Can GAN be the future?

GANs have improved over time, and despite all of the challenges posed by the past decade of research, GANs have generated content that will become increasingly difficult to distinguish from real content. When comparing image generation in 2014 to today, the quality was not expected to improve that much, and if the progress continues in this direction, GANs will remain a very important research project in the future, assuming GANs’ acceptance.

We don’t know “what GANs can do for us” because we’re still talking about “what we can do for GANs” to make them more stable, but the future of GANs appears bright for humanity, and we could see machine-generated code, music, videos, and even essays and blogs in no time. However, I can assure you that this blog post wasn’t written by a GAN (or was it?).

Applications of GANS

• GANs are being used by many scholars and researchers from famous institutions and laboratories to develop drugs for cancer, dermatological disorders, fibrosis, Parkinson’s, Alzheimer’s, ALS, diabetes, sarcopenia, and aging.
• One particularly interesting application will be in the dental department, where it is believed that researchers are using GANs to manufacture dental crowns, which will speed up the entire process for the patient because a procedure that previously took weeks could now be completed with high precision in just a few hours.
• GANs are also being used to improve augmented reality (AR) scenes in specific settings, such as completing missing environment maps by learning the statistical structure of the world utilizing the creative generating capabilities of GANs. Other AR-related GAN use cases that include environment texturing, such as enabling, lighting, and reflections, are also handled.
• Another application where GANs will show their worth is in the generation of training data for low-data regimes. For example, a research team at Apple demonstrated that you can feed a large amount of unlabeled data to a refiner powered by GANs, which can then be trained to generate more realistic training data given some base labeled synthetic data, and this technique can reduce the cost of generating supervised datasets and help on a variety of machine learning tasks.
• There are many interesting research topics involving GANs, such as improving disentanglement, applying contrastive learning, and training more stable GANs, that have yet to be addressed with the help of various building tools that enable practitioners and researchers to quickly move from proof-of-concept to real-world applications, inspiring more creative use of GANs in the future.
• Differential private GANs are a significant issue to explore in terms of data privacy, and there are many prospects for training more efficient models for quick data rendering and supporting multimodal data kinds, as is the case with difficult situations like self-driving automobiles.