  Machine unlearning has emerged as a new paradigm to deliberately forget data
samples from a given model in order to adhere to stringent regulations.
However, existing machine unlearning methods have been primarily focused on
classification models, leaving the landscape of unlearning for generative
models relatively unexplored. This paper serves as a bridge, addressing the gap
by providing a unifying framework of machine unlearning for image-to-image
generative models. Within this framework, we propose a
computationally-efficient algorithm, underpinned by rigorous theoretical
analysis, that demonstrates negligible performance degradation on the retain
samples, while effectively removing the information from the forget samples.
Empirical studies on two large-scale datasets, ImageNet-1K and Places-365,
further show that our algorithm does not rely on the availability of the retain
samples, which further complies with data retention policy. To our best
knowledge, this work is the first that represents systemic, theoretical,
empirical explorations of machine unlearning specifically tailored for
image-to-image generative models. Our code is available at
https://github.com/jpmorganchase/l2l-generator-unlearning.
