Abstract: A computer-implemented method is used to adapt a first artificial neural network for data classification tasks. The first artificial neural network is characterized by a first number of first weight parameters, and includes a set of first network layers. The method includes freezing at least some of the first weight parameters of the first neural network to obtain frozen first weight parameters and duplicating the frozen first weight parameters to obtain duplicated first weight parameters. A second artificial neural network is applied to the duplicated first weight parameters to obtain modulated first weight parameters. The second artificial neural network is characterized by a second number of second weight parameters, the second number being smaller than the first number. The frozen first weight parameters are replaced in the first neural network with the modulated first weight parameters to obtain a modulated first artificial neural network adapted for a data classification task.

Abstract: A neural network conversion method and a recognition apparatus that implements the method are provided. A method of converting an analog neural network (ANN) to a spiking neural network (SNN) normalizes first parameters of a trained ANN based on a reference activation that is set to be proximate to a maximum activation of artificial neurons included in the ANN, and determines second parameters of an SNN based on the normalized first parameters.

Abstract: A neural network conversion method and a recognition apparatus that implements the method are provided. A method of converting an analog neural network (ANN) to a spiking neural network (SNN) normalizes first parameters of a trained ANN based on a reference activation that is set to be proximate to a maximum activation of artificial neurons included in the ANN, and determines second parameters of an SNN based on the normalized first parameters.