Efficient Estimation of Influence of a Training Instance (2020)

Abstract: 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.

## introduction what is the influence of a training instance on a machine learning model? this question has attracted the attention of the community @xcite @xcite . evaluating the influence of a training instance leads to more interpretable models and other applications like data cleansing. a simple evaluation is by comparing a model with another similarly trained model, whose training does not include the instance of interest. this method, however, requires computational costs of time and storage depending on the number of instances, which indicates the extreme difficulty ( we propose a trick for enabling a neural network without restrictions to estimate the influence, which we refer to as turn-over dropout. this method is computationally efficient as it requires only running two forward computations after training a single model on the entire training dataset. in addition to the efficiency, we demonstrated that it enabled bert @xcite and vg-gnet @xcite to analyze the influences of training through various experiments, including example-based interpretation of error predictions and data cleansing to improve the accuracy on a test set with a distributional shift. ## conclusion this paper proposed a method that required a low computational cost for estimating the influence of a training instance. the method alters dropout with instance-specific masks and, for estimation, uses sub-networks that are not trained with each instance. the experiments demonstrated that this method could be applied even for complex models.