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+methods%252C+is+overlooked.+In+this+paper%252C+we+fill+this+gap+by+first+proposing+the+framework+of+Machine+Unlearning+for+Contrastive+learning+(MUC)+and+adapting+existing+methods.+Furthermore%252C+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%252C+MoCo+and+CLIP.+We+observe+that+AC+addresses+drawbacks+of+existing+methods%3a+(1)+achieving+state-of-the-art+performance+and+approximating+exact+unlearning+(retraining)%253B+(2)+allowing+data+owners+to+clearly+visualize+the+effect+caused+by+unlearning+through+black-box+auditing.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.d2165bfc07d1d320e1102033a2c4589e%3frik%3dJviYPp3km9%252fVjg%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Beta+Calibration+Machine+Learning&FORM=IRPRST&ck=71CC371D30C91D4CCA27BCEF3CC191F0&selectedIndex=10&itb=0)
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