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%20methods%2C%20is%20overlooked.%20In%20this%20paper%2C%20we%20fill%20this%20gap%20by%20first%20proposing%20the%20framework%20of%20Machine%20Unlearning%20for%20Contrastive%20learning%20(MUC)%20and%20adapting%20existing%20methods.%20Furthermore%2C%20we%20observe%20that%20several%20methods%20are%20mediocre%20unlearners%20and%20existing%20auditing%20tools%20may%20not%20be%20sufficient%20for%20data%20owners%20to%20validate%20the%20unlearning%20effects%20in%20contrastive%20learning.%20We%20thus%20propose%20a%20novel%20method%20called%20Alignment%20Calibration%20(AC)%20by%20explicitly%20considering%20the%20properties%20of%20contrastive%20learning%20and%20optimizing%20towards%20novel%20auditing%20metrics%20to%20easily%20verify%20unlearning.%20We%20empirically%20compare%20AC%20with%20baseline%20methods%20on%20SimCLR%2C%20MoCo%20and%20CLIP.%20We%20observe%20that%20AC%20addresses%20drawbacks%20of%20existing%20methods:%20(1)%20achieving%20state-of-the-art%20performance%20and%20approximating%20exact%20unlearning%20(retraining)%3B%20(2)%20allowing%20data%20owners%20to%20clearly%20visualize%20the%20effect%20caused%20by%20unlearning%20through%20black-box%20auditing.)
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