How can GANs help improve models?

DL requires a lot of data to mine insights and make an informed decision. The success of DL to generalize well is mainly attributed to the training of NN architectures on large amounts of data. However, it is not always possible to acquire more data because of several reasons, as explained earlier. What if we can generate synthetic data that is modeled around real-world data so that we can augment the limited datasets and improve our model predictions? Synthetic data has a multitude of use cases in DL because of the infinite variations of synthetic data that can be produced. DL is the primary beneficiary of synthetic data, and research shows that enhancing real-world data with synthetic data produced using generative models such as GANs can significantly improve model fitness and thereby result in better predictions. GANs can help improve models directly and indirectly through the generation of synthetic data, which can make sensitive data accessible...