  Identifying the influence of training data for data cleansing can improve the
accuracy of deep learning. An approach with stochastic gradient descent (SGD)
called SGD-influence to calculate the influence scores was proposed, but, the
calculation costs are expensive. It is necessary to temporally store the
parameters of the model during training phase for inference phase to calculate
influence sores. In close connection with the previous method, we propose a
method to reduce cache files to store the parameters in training phase for
calculating inference score. We only adopt the final parameters in last epoch
for influence functions calculation. In our experiments on classification, the
cache size of training using MNIST dataset with our approach is 1.236 MB. On
the other hand, the previous method used cache size of 1.932 GB in last epoch.
It means that cache size has been reduced to 1/1,563. We also observed the
accuracy improvement by data cleansing with removal of negatively influential
data using our approach as well as the previous method. Moreover, our simple
and general proposed method to calculate influence scores is available on our
auto ML tool without programing, Neural Network Console. The source code is
also available.
