Can Synthetic Data Solve The Bulk Data Problem In Deep Learning?

Synthetic data generation has become a surrogate technique for tackling the problem of bulk data needed in training deep learning algorithms. Areas such as computer vision have greatly benefited from advances in deep learning and now generating synthetic data is serving as a good starting point for researchers who are trying to bridge the data gap. A recent research from University of Barcelona talks about Synthetic Data Generation model which introduced a synthetic image generation algorithm to tackle the lack of availability of training data in a fully-supervised learning problem. Synthetic data is defined as anonymised data, generated to mimic real world data.