  Large pre-trained generative models are known to occasionally output
undesirable samples, which undermines their trustworthiness. The common way to
mitigate this is to re-train them differently from scratch using different data
or different regularization -- which uses a lot of computational resources and
does not always fully address the problem.
  In this work, we take a different, more compute-friendly approach and
investigate how to post-edit a model after training so that it ''redacts'', or
refrains from outputting certain kinds of samples. We show that redaction is a
fundamentally different task from data deletion, and data deletion may not
always lead to redaction. We then consider Generative Adversarial Networks
(GANs), and provide three different algorithms for data redaction that differ
on how the samples to be redacted are described. Extensive evaluations on
real-world image datasets show that our algorithms out-perform data deletion
baselines, and are capable of redacting data while retaining high generation
quality at a fraction of the cost of full re-training.
