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+approach%252C+where+the+out-of-distribution+data+is+a+huge+collective+of+random+images%252C+or+as+a+fully+self-supervised+learning+approach%252C+where+the+out-of-distribution+data+is+self-generated+by+applying+distribution-shifting+transformations.+The+ability+to+incorporate+additional+out-of-distribution+samples+enables+a+feasible+solution+for+datasets+where+AD+methods+based+on+contrastive+learning+generally+underperform%252C+such+as+aerial+images+or+microscopy+images.+Furthermore%252C+the+high-quality+features+learned+through+contrastive+learning+consistently+enhance+performance+in+OE+scenarios%252C+even+when+the+available+out-of-distribution+dataset+is+not+diverse+enough.+Our+extensive+experiments+demonstrate+the+superiority+of+our+proposed+method+under+various+scenarios%252C+including+unimodal+and+multimodal+settings%252C+with+various+image+datasets.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.0a37c50a23989fb17ed6badf7da84e9a%3frik%3dG9FLgthcap%252f%252fGw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Sample+Distance+Distribution+Diagram+for+Contrastive+Representation+Learning&FORM=IRPRST&ck=FF3FDA64C7A96E1B5678396632FEA74D&selectedIndex=0&itb=0)
