  Machine unlearning is a complex process that necessitates the model to
diminish the influence of the training data while keeping the loss of accuracy
to a minimum. Despite the numerous studies on machine unlearning in recent
years, the majority of them have primarily focused on supervised learning
models, leaving research on contrastive learning models relatively
underexplored. With the conviction that self-supervised learning harbors a
promising potential, surpassing or rivaling that of supervised learning, we set
out to investigate methods for machine unlearning centered around contrastive
learning models. In this study, we introduce a novel gradient constraint-based
approach for training the model to effectively achieve machine unlearning. Our
method only necessitates a minimal number of training epochs and the
identification of the data slated for unlearning. Remarkably, our approach
demonstrates proficient performance not only on contrastive learning models but
also on supervised learning models, showcasing its versatility and adaptability
in various learning paradigms.
