Method for training a convolutional neural network comprising nodes arranged in layers and a pruning mask

A non-transitory computer readable data carrier on which is stored program code of a computer program for training a convolutional neural network including nodes arranged in layers and a pruning mask, the program code, when executed by a computer, causing the computer to perform the following steps:

• providing at least one set of labeled training data;
• initializing the convolutional neural network including at least one convolutional layer and a pruning mask following the convolutional layer;
• passing the training data through the convolutional neural network;
• computing a loss function and comparing the loss function with labels of the training data to quantify the convolutional neural networks process;
• and
• minimizing the loss function by backward propagation including determining a gradient of the loss function for determining a direction for optimizing the nodes of the convolutional neural network;
• wherein the passing of the training data through the pruning mask includes multiplying structures si of input of the pruning mask with pruning parameters Mi, the pruning parameters Mi for the structures si being 0 or 1;
• wherein the pruning mask is approximated by an approximation function during backward propagation.

A system for training a convolutional neural network using a pruning mask, wherein the system comprising:

• a computer configured to train a convolutional neural network including nodes arranged in layers and a pruning mask, the computer configured to:
• provide at least one set of labeled training data,
• initialize the convolutional neural network including at least one convolutional layer and a pruning mask following the convolutional layer,
• pass the training data through the convolutional neural network,
• compute a loss function and comparing the loss function with labels of the training data to quantify the convolutional neural networks process, and
• minimize the loss function by backward propagation including determining a gradient of the loss function for determining a direction for optimizing the nodes of the convolutional neural network,
• wherein the passing of the training data through the pruning mask includes multiplying structures si of input of the pruning mask with pruning parameters Mi, the pruning parameters Mi for the structures si being 0 or 1,
• wherein the pruning mask is approximated by an approximation function during backward propagation.