TY - JOUR
T1 - Special Issue on Deep Representation and Transfer Learning for Smart and Connected Health
AU - PALADE, VASILE
AU - WERMTER, STEFAN
AU - RUIZ-GARCIA, ARIEL
AU - DE PADUA BRAGA, ANTONIO
AU - Cheong Took, Clive
PY - 2021/2/4
Y1 - 2021/2/4
N2 - 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
AB - 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
U2 - 10.1109/TNNLS.2021.3049931
DO - 10.1109/TNNLS.2021.3049931
M3 - Editorial
SN - 2162-2388
VL - 32
SP - 464
EP - 465
JO - IEEE Transactions on Neural Networks and Learning Systems
JF - IEEE Transactions on Neural Networks and Learning Systems
IS - 2
ER -