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[158] A Mathematical Framework for Transformer Circuits

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[156] Interpreting CLIP's Image Representation via Text-Based Decomposition

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[157] LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity

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[154] Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment

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[153] Contrastive Explanations for Model Interpretability

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[151] FOIL it! Find One mismatch between Image and Language caption

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[150] Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

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[147] Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers

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[146] Transformer Interpretability Beyond Attention Visualization

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[77] Interpretable Image Classification with Differentiable Prototype Assignment

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[75] SESS: Saliency Enhancing with Scaling and Sliding

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