Knowledge transfer between different deep learning architectures

Abstract

The invention relates to a method for converting a first neural network with a first architecture into a second neural network with a second architecture for use in a vehicle controller in order to obtain the knowledge of the first neural network and transfer same to the second neural network. In a first step of the method, a conversion () of at least one layer of the first neural network into at least one layer of the second neural network is carried out. In a second step, a random initialization () of the at least one converted layer is carried out in the architecture of the second neural network. In a third step, a training process () of the at least one converted layer is carried out in the second neural network. In a fourth step, a fine-tuning process () of the non-converted layer is carried out in the second neural network or in the entire second neural network. The conversion of the first neural network into the second neural network is carried out in multiple cycles or iterations, wherein for each cycle, the conversion (), random initialization (), training (), and simultaneous fine-tuning () steps are carried out.