Nowadays machine learning is acting as a heavy loader tool to help us solve problems related to the computer vision department such as image classification, segmentation, processing and many others. Most of these kinds of applications rely on training the neural network in a supervised passion where the labels are available. When it comes to synthetically generated data, these labels are determined by human interpretation. The main challenge is that training our neural network on such synthetic data does not always generalize on real data i,e the targeted data. Even at this point, we can also have a synthetic model which can perform better on real data but that requires careful construction of training set and inclusion of real noise and some features from the real dataset, but practically synthetic and real data are drawn from a different distribution like synthetic data can be generated by using GAN's which is a function of latent space and it is very essential for the success of neural network that both data should have drawn from the same distribution.