تقرير
LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity
| العنوان: | LeGrad: An Explainability Method for Vision Transformers via Feature Formation Sensitivity |
|---|---|
| المؤلفون: | Bousselham, Walid, Boggust, Angie, Chaybouti, Sofian, Strobelt, Hendrik, Kuehne, Hilde |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition |
| الوصف: | 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 the last as well as intermediate tokens to produce the merged explainability map. This makes LeGrad a conceptually simple and an easy-to-implement tool for enhancing the transparency of ViTs. We evaluate LeGrad in challenging segmentation, perturbation, and open-vocabulary settings, showcasing its versatility compared to other SotA explainability methods demonstrating its superior spatial fidelity and robustness to perturbations. A demo and the code is available at https://github.com/WalBouss/LeGradTest. Comment: Code available at https://github.com/WalBouss/LeGradTest |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2404.03214Test |
| رقم الانضمام: | edsarx.2404.03214 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |