Previously, we have introduced the idea of neural network transfer, where learning on a target problem is sped up by using the weights obtained from a network trained for a related source task. Here, we present a new algorithm. Several experiments demonstrate that target networks initialized via DBT learn significantly faster than networks initialized randomly.

Neural networks are usually trained from scratch, relying only on the training data for guidance. However, as more and more networks are trained for various tasks, it becomes reasonable to seek out methods that.

Neural networks are usually trained from scratch, relying only on the training data for guidance. However, as more and more networks are trained for various tasks, it becomes reasonable to seek out methods that.

Neural networks are usually trained from scratch, relying only on the training data for guidance. However, as more and more networks are trained for various tasks, it becomes reasonable to seek out methods that.