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The lack of interpretability of the Vision Transformer may hinder its use incritical real-world applications despite its effectiveness. To overcome thisissue, we propose a post-hoc interpretability method called VISION DIFFMASK,which uses the activations of the model's hidden layers to predict the relevantparts of the input that contribute to its final predictions. Our approach usesa gating mechanism to identify the minimal subset of the original input thatpreserves the predicted distribution over classes. We demonstrate thefaithfulness of our method, by introducing a faithfulness task, and comparingit to other state-of-the-art attribution methods on CIFAR-10 and ImageNet-1K,achieving compelling results. To aid reproducibility and further extension ofour work, we open source our implementation:https://github.com/AngelosNal/Vision-DiffMask

VISION DIFFMASK: Faithful Interpretation of Vision Transformers with Differentiable Patch Masking