  Machine unlearning aims to eliminate the influence of a subset of training
samples (i.e., unlearning samples) from a trained model. Effectively and
efficiently removing the unlearning samples without negatively impacting the
overall model performance is still challenging. In this paper, we propose a
contrastive unlearning framework, leveraging the concept of representation
learning for more effective unlearning. It removes the influence of unlearning
samples by contrasting their embeddings against the remaining samples so that
they are pushed away from their original classes and pulled toward other
classes. By directly optimizing the representation space, it effectively
removes the influence of unlearning samples while maintaining the
representations learned from the remaining samples. Experiments on a variety of
datasets and models on both class unlearning and sample unlearning showed that
contrastive unlearning achieves the best unlearning effects and efficiency with
the lowest performance loss compared with the state-of-the-art algorithms.
