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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%2C increasing attribution accuracy and improving the interpretability of such models when applied to safety-critical domains such as healthcare. Crucially%2C unlike commonly used unimodal attribution methods%2C M2IB does not require ground truth labels%2C 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%2C we demonstrate the effectiveness of M2IB attribution and show that it outperforms gradient-based%2C perturbation-based%2C and attention-based attribution methods both qualitatively and quantitatively.?quality=80&w=800)
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