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+models+are+increasingly+accessible+in+public+repositories%252C+i.e.%252C+model+zoos.+Given+a+new+prediction+task%252C+finding+the+best+model+to+fine-tune+can+be+computationally+intensive+and+costly%252C+especially+when+the+number+of+pre-trained+models+is+large.+Selecting+the+right+pre-trained+models+is+crucial%252C+yet+complicated+by+the+diversity+of+models+from+various+model+families+(like+ResNet%252C+Vit%252C+Swin)+and+the+hidden+relationships+between+models+and+datasets.+Existing+methods%252C+which+utilize+basic+information+from+models+and+datasets+to+compute+scores+indicating+model+performance+on+target+datasets%252C+overlook+the+intrinsic+relationships%252C+limiting+their+effectiveness+in+model+selection.+In+this+study%252C+we+introduce+TransferGraph%252C+a+novel+framework+that+reformulates+model+selection+as+a+graph+learning+problem.+TransferGraph+constructs+a+graph+using+extensive+metadata+extracted+from+models+and+datasets%252C+while+capturing+their+inherent+relationships.+Through+comprehensive+experiments+across+16+real+datasets%252C+both+images+and+texts%252C+we+demonstrate+TransferGraph%27s+effectiveness+in+capturing+essential+model-dataset+relationships%252C+yielding+up+to+a+32%2525+improvement+in+correlation+between+predicted+performance+and+the+actual+fine-tuning+results+compared+to+the+state-of-the-art+methods.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.3598b355a6b32e3c01c7aeaee894e811%3frik%3d%252fM4Ha%252fiUk0ZbXA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Machine+Learning+Model+Zoo&FORM=IRPRST&ck=80B3C7A940EE7ED980BD1B7FC0332433&selectedIndex=25&itb=0)
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