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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 challenging. Existing works mainly exploit input and output space and classification loss, which can result in ineffective unlearning or performance loss. In addition, they utilize unlearning or remaining samples ineffectively, sacrificing either unlearning efficacy or efficiency. Our main insight is that the direct optimization on the representation space utilizing both unlearning and remaining samples can effectively remove influence of unlearning samples while maintaining representations learned from remaining samples. 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' embeddings so that their embeddings are closer to the embeddings of unseen 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. In addition, it is generalizable to different contrastive frameworks and other models such as vision-language models. Our main code is available on github.com/Emory-AIMS/Contrastive-Unlearning