  Understanding the influence of a training instance on a neural network model
leads to improving interpretability. However, it is difficult and inefficient
to evaluate the influence, which shows how a model's prediction would be
changed if a training instance were not used. In this paper, we propose an
efficient method for estimating the influence. Our method is inspired by
dropout, which zero-masks a sub-network and prevents the sub-network from
learning each training instance. By switching between dropout masks, we can use
sub-networks that learned or did not learn each training instance and estimate
its influence. Through experiments with BERT and VGGNet on classification
datasets, we demonstrate that the proposed method can capture training
influences, enhance the interpretability of error predictions, and cleanse the
training dataset for improving generalization.
