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

arXiv:2404.03214v1 Announce Type: new
Abstract: Vision Transformers (ViTs), with their ability to model long-range dependencies through self-attention mechanisms, have become a standard architecture in computer vision. However, the interpretability of these models remains a challenge. To address this, we propose LeGrad, an explainability method specifically designed for ViTs. LeGrad computes the gradient with respect to the attention maps of ViT layers, considering the gradient itself as the explainability signal. We aggregate the signal over all layers, combining the activations of …

arxiv cs.cv explainability feature sensitivity transformers type via vision vision transformers