Fast Evaluation of Deep Neural Network (DNN) for Past Dataset in Incremental Learning

During the operation of a system including a deep neural network (DNN), new input values not included in the training dataset are given to the DNN. In such a case, the DNN may be incrementally trained with the new input values; however, additional training may reduce the accuracy of the DNN in regard to the dataset that was previously obtained and used for the past training. The effect of the additional training on the accuracy for the past dataset needs to be evaluated, but evaluation by testing all the input values included in the past dataset takes time. Therefore, we propose a new method to evaluate the effect on the accuracy for the past dataset, in which the gradient of the parameter values (such as weight and bias) for the past dataset is extracted by running the DNN before the training. After the training, the effect on the accuracy with respect to the past dataset can be calculated fast from the gradient and update differences of the parameter values. To show the usefulness of the proposed method, we present experimental results with several datasets. The results show that the proposed method can estimate the accuracy change by additional training in a short constant time.