  A concept of using Neural Ordinary Differential Equations(NODE) for Transfer
Learning has been introduced. In this paper we use the EfficientNets to explore
transfer learning on CIFAR-10 dataset. We use NODE for fine-tuning our model.
Using NODE for fine tuning provides more stability during training and
validation.These continuous depth blocks can also have a trade off between
numerical precision and speed .Using Neural ODEs for transfer learning has
resulted in much stable convergence of the loss function.
