Data Redaction from Pre-trained GANs
Zhifeng Kong, Kamalika Chaudhuri · 2023
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 Adversar-ial 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,