Motivated by our observations, we use CW-DeepNNK to propose a novel early stopping criterion that (i) does not require a validation set, (ii) is based on a task performance metric, and (iii) allows stopping to be reached at different points for each channel.
Multi-modal fusion has been proved to help enhance the performance of scene classification tasks.
It is widely known that very small datasets produce overfitting in Deep Neural Networks (DNNs), i. e., the network becomes highly biased to the data it has been trained on.
Convolutional neural networks (CNNs) have demonstrated their capability to solve different kind of problems in a very huge number of applications.
The main challenge in Super Resolution (SR) is to discover the mapping between the low- and high-resolution manifolds of image patches, a complex ill-posed problem which has recently been addressed through piecewise linear regression with promising results.