Channel scaling: a scale-and-select approach for selective transfer learning

Abstract

A computer-implemented method, a computer program product, and a computer system for introducing channel-scaling layers in a deep neural network. A computer receives a pre-trained deep neural network including convolutional layers followed by respective ones of activation layers, adds channel-scaling layers after the respective ones of the activation layers, where each of the channel-scaling layers includes scaling weights. The computer trains the scaling weights in the channel-scale layers. The computer removes, in the convolutional layers, channels whose corresponding scaling weights are lower than a predetermined threshold. The computer removes the channel-scaling layers. In response to determining that at least one convergence criterion is met, the computer provides a finally trained deep neural network.