Neural network execution block and transfer learning

Abstract

Systems, architectures, and approaches for use with neural networks. An execution block and a system architecture using a novel execution block are disclosed along with how such an execution block can be used. The execution block uses a fully connected stack of layers and parameters of this fully connected stack of layers are shared. The fully connected nature of the block and on-the-fly generated parameters allow for bypassing specialized training data sets. The system may be trained using non-task-specific training data sets and this allows the system to transfer what is learned to execute a different task. Thus, instead of having to obtain a specialized training data set for a specific task, a more generic training data set can be used to train and prepare the system for that specific task. Results have shown that performance is as good as than the state of the art in providing solutions.