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+aims+to+adapt+a+pre-trained+source+model+to+an+unlabeled+target+domain+with+no+access+to+the+source+data.+Inspired+by+the+success+of+pre-trained+large+vision-language+(ViL)+models+in+many+other+applications%252C+the+latest+SFDA+methods+have+also+validated+the+benefit+of+ViL+models+by+leveraging+their+predictions+as+pseudo+supervision.+However%252C+we+observe+that+ViL%27s+predictions+could+be+noisy+and+inaccurate+at+an+unknown+rate%252C+potentially+introducing+additional+negative+effects+during+adaption.+To+address+this+thus-far+ignored+challenge%252C+in+this+paper%252C+we+introduce+a+novel+Proxy+Denoising+(ProDe)+approach.+Specifically%252C+we+leverage+the+ViL+model+as+a+proxy+to+facilitate+the+adaptation+process+towards+the+latent+domain-invariant+space.+Critically%252C+we+design+a+proxy+denoising+mechanism+for+correcting+ViL%27s+predictions.+This+is+grounded+on+a+novel+proxy+confidence+theory+by+modeling+elegantly+the+domain+adaption+effect+of+the+proxy%27s+divergence+against+the+domain-invariant+space.+To+capitalize+the+corrected+proxy%252C+we+further+derive+a+mutual+knowledge+distilling+regularization.+Extensive+experiments+show+that+our+ProDe+significantly+outperforms+the+current+state-of-the-art+alternatives+under+both+conventional+closed-set+setting+and+the+more+challenging+open-set%252C+partial-set+and+generalized+SFDA+settings.+The+code+will+release+soon.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.ac811d859138b4e975fffff74806f328%3frik%3dLO7zNXSIgvaUYw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Domain+by+Proxy&FORM=IRPRST&ck=4E80C98DD101B388069C3F11114E0EE9&selectedIndex=8&itb=0)
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