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+approach+that+learns+latent+representations+that+compress+irrelevant+information+while+preserving+relevant+visual+and+textual+features.+We+demonstrate+how+M2IB+can+be+applied+to+attribution+analysis+of+vision-language+pretrained+models%252C+increasing+attribution+accuracy+and+improving+the+interpretability+of+such+models+when+applied+to+safety-critical+domains+such+as+healthcare.+Crucially%252C+unlike+commonly+used+unimodal+attribution+methods%252C+M2IB+does+not+require+ground+truth+labels%252C+making+it+possible+to+audit+representations+of+vision-language+pretrained+models+when+multiple+modalities+but+no+ground-truth+data+is+available.+Using+CLIP+as+an+example%252C+we+demonstrate+the+effectiveness+of+M2IB+attribution+and+show+that+it+outperforms+gradient-based%252C+perturbation-based%252C+and+attention-based+attribution+methods+both+qualitatively+and+quantitatively.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.27546a531094f020c1fdbf379358f914%3frik%3d00%252f%252fbHztYRvt3w%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Textual+Explanations+of+Visual+Models&FORM=IRPRST&ck=8AD7F94C27BFA9E0239E6EBD46721E0F&selectedIndex=5&itb=0)
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