Guest Editorial: Special Issue on Deep Representation and Transfer Learning for Smart and Connected Health

Deep neural networks (NNs) have been proved to be efficient learning systems for supervised and unsupervised tasks. However, learning complex data representations using deep NNs can be difficult due to problems such as lack of data, exploding or vanishing gradients, high computational cost, or incorrect parameter initialization, among others. Deep representation and transfer learning (RTL) can facilitate the learning of data representations by taking advantage of transferable features learned by an NN model in a source domain, and adapting the model to a new domain.