Use Cases of Generative Adversarial Networks
Generative Adversarial Networks (GANs) are most popular for generating images from a given dataset of images but apart from it, GANs are now being used for a variety of applications. These are a class of neural networks that has a discriminator block and a generator block that works together and is able to produce new samples apart from just classifying or predicting the class of sample.
Some of the newly discovered uses cases of GANs are:
Artificial intelligence has proved to be a boon to many industries but it is also surrounded by the problem of Cyber threats.GANs are proved to be a great help to handle adversarial attacks. The adversarial attacks use a variety of techniques to fool deep learning architectures. By creating fake examples and training the model to identify them we counter these attacks.
Generating Data using GANs:
Data is the most important key for any deep learning algorithm. In general, the more is the data, the better is the performance of any deep learning algorithm. But in many cases such as health diagnostics, the amount of data is restricted, in such cases, there is a need to generate good quality data. For which GANs are being used.
There are many cases when our data needs to be kept confidential. This is especially useful in defense and military applications. We have many data encryption schemes but each has its own limitations, in such a case GANs can be useful. Recently, in 2016, Google opened a new research path on using GAN competitive framework for encryption problems, where two networks had to compete in creating the code and cracking it.
We can use GANs for pseudo style transfer i.e. modifying a part of the subject, without complete style transfer. For e.g. in many applications, we want to add a smile to an image, or just work on the eyes part of the image. This can also be extended to other domains such as Natural Language Processing, speech processing, etc. For e.g. we can work on some selected words of a paragraph without modifying the whole paragraph.