Reviews: Representer Point Selection for Explaining Deep Neural Networks

This paper proposes a decomposition of the pre-activation prediction (the values of the last intermediate layer in a NN) into a linear combination of activations of the training points. The weights in this linear combination are called representer values. Positive representer values represent excitatory signals that contribute to the prediction of the particular sample in the corresponding class, while negative representer values inhibit the prediction to that particular class. The representer values can be used to better understand the prediction of the model. The experimental section shows how this technique can be used in several explanatory analyzes, such as: * Data debugging: for a MNIST dataset, consider some of the labels being interchanged in the dataset (in the example used some 1s become 7s).