A new weight freezing method for reducing training time in designing artificial neural networks

The paper presents a novel weight freezing (NWF) method to reduce training time in designing artificial neural networks (ANNs). The idea behind NWF is to freeze input weights of a hidden node when its output does not change much in the successive few training epochs. Theoretical and experimental studies reveal that some hidden nodes of an ANN maintain almost constant output after some training epochs, while others continuously change during the whole training period. Our preliminary results indicate the effectiveness of NWF to reduce training time in designing ANNs.