Abstract:
We introduce a method called TracIn that computes the influence of a training
example on a prediction made by the model. The idea is to trace how the loss on
the test point changes during the training process whenever the training example of
interest was utilized. We provide a scalable implementation of TracIn via: (a) a
first-order gradient approximation to the exact computation, (b) saved checkpoints
of standard training procedures, and (c) cherry-picking layers of a deep neural
network. In contrast with previously proposed methods, TracIn is simple to
implement; all it needs is the ability to work with gradients, checkpoints, and loss
functions. The method is general. It applies to any machine learning model trained
using stochastic gradient descent or a variant of it, agnostic of architecture, domain
and task. We expect the method to be widely useful within processes that study
and improve training data.
