  Machine unlearning provides viable solutions to revoke the effect of certain
training data on pre-trained model parameters. Existing approaches provide
unlearning recipes for classification and generative models. However, a
category of important machine learning models, i.e., contrastive learning (CL)
methods, is overlooked. In this paper, we fill this gap by first proposing the
framework of Machine Unlearning for Contrastive learning (MUC) and adapting
existing methods. Furthermore, we observe that several methods are mediocre
unlearners and existing auditing tools may not be sufficient for data owners to
validate the unlearning effects in contrastive learning. We thus propose a
novel method called Alignment Calibration (AC) by explicitly considering the
properties of contrastive learning and optimizing towards novel auditing
metrics to easily verify unlearning. We empirically compare AC with baseline
methods on SimCLR, MoCo and CLIP. We observe that AC addresses drawbacks of
existing methods: (1) achieving state-of-the-art performance and approximating
exact unlearning (retraining); (2) allowing data owners to clearly visualize
the effect caused by unlearning through black-box auditing.
