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+is+to+eliminate+previously+learned+data+from+a+model.+However%252C+it+is+challenging+to+strike+a+balance+between+computation+cost+and+performance+when+using+existing+MU+techniques.+Taking+inspiration+from+the+influence+of+label+smoothing+on+model+confidence+and+differential+privacy%252C+we+propose+a+simple+gradient-based+MU+approach+that+uses+an+inverse+process+of+label+smoothing.+This+work+introduces+UGradSL%252C+a+simple%252C+plug-and-play+MU+approach+that+uses+smoothed+labels.+We+provide+theoretical+analyses+demonstrating+why+properly+introducing+label+smoothing+improves+MU+performance.+We+conducted+extensive+experiments+on+six+datasets+of+various+sizes+and+different+modalities%252C+demonstrating+the+effectiveness+and+robustness+of+our+proposed+method.+The+consistent+improvement+in+MU+performance+is+only+at+a+marginal+cost+of+additional+computations.+For+instance%252C+UGradSL+improves+over+the+gradient+ascent+MU+baseline+by+66%2525+unlearning+accuracy+without+sacrificing+unlearning+efficiency.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.1f2ba760cc6493f66be1dff5e96023e4%3frik%3drZtYx6e5DlQ0xQ%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Smoothing+in+Machine+Learning+Diagram&FORM=IRPRST&ck=87BC6A0F2F0A1C6B6050A9DA596E2D87&selectedIndex=7&itb=0)
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