Training of Deep Learning (DL) models requires a large amount of data in the first place and the data set must be sufficiently diverse for the network to be transferable such that it produces unbiased predictions. At the same time, the data size needs to be balanced to compensate for the cost of their generation. Strategies to deal with scarce data problems include Transfer Learning (TL), Self-Supervised Learning, Generative Adversarial Networks (GANs), Model Architecture, Physics-Informed Neural Network, and Deep Synthetic Minority Oversampling techniques to name a few recent approaches, as pointed out in .