  Identifying harmful instances, whose absence in a training dataset improves
model performance, is important for building better machine learning models.
Although previous studies have succeeded in estimating harmful instances under
supervised settings, they cannot be trivially extended to generative
adversarial networks (GANs). This is because previous approaches require that
(1) the absence of a training instance directly affects the loss value and that
(2) the change in the loss directly measures the harmfulness of the instance
for the performance of a model. In GAN training, however, neither of the
requirements is satisfied. This is because, (1) the generator's loss is not
directly affected by the training instances as they are not part of the
generator's training steps, and (2) the values of GAN's losses normally do not
capture the generative performance of a model. To this end, (1) we propose an
influence estimation method that uses the Jacobian of the gradient of the
generator's loss with respect to the discriminator's parameters (and vice
versa) to trace how the absence of an instance in the discriminator's training
affects the generator's parameters, and (2) we propose a novel evaluation
scheme, in which we assess harmfulness of each training instance on the basis
of how GAN evaluation metric (e.g., inception score) is expect to change due to
the removal of the instance. We experimentally verified that our influence
estimation method correctly inferred the changes in GAN evaluation metrics.
Further, we demonstrated that the removal of the identified harmful instances
effectively improved the model's generative performance with respect to various
GAN evaluation metrics.
