[PDF] L1-Norm Gradient Penalty for Noise Reduction of Attribution Maps
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We developed a new reg- ularization method named L1-Norm Gradient Penalty to re- move the noise from attribution maps. This method regu- larized attribution ...
To obtain sparse and interpretable attribution maps, we developed a new regularization method that includes a penalty term, based on the L1-norm of gradient ...
A new regularization method is developed that includes a penalty term, based on the L1-norm of gradient values calculated through back-propagation ...
To obtain sparse and interpretable attribution maps, we developed a new regularization method that includes a penalty term, based on the L1 -norm of gradient ...
Aug 2, 2024 · L1-Norm Gradient Penalty for Noise Reduction of Attribution Maps. CVPR Workshops 2019: 118-121. [c12]. view. electronic edition via DOI ...
L1-Norm Gradient Penalty for Noise Reduction of Attribution Maps. K Kiritoshi, R Tanno, T Izumitani. Proceedings of the IEEE Conference on Computer Vision ...
Nov 27, 2022 · a technique for smoothing out the gradients using a Gaussian kernel, to pro- duce saliency maps with reduced noise and more coherent ...
Missing: Penalty | Show results with:Penalty
Quantitatively Assessing Feature Attribution Methods. arXiv preprint arXiv:2109.15035, 2021. 2, 4. L1-Norm Gradient Penalty for Noise Reduction of Attribution ...
This is similar to [17], who propose a loss function which enforces an L1 penalty on the learned function gradient to ensure the final model has sparse ...
L1-Norm Gradient Penalty for Noise Reduction of Attribution Maps. Keisuke Kiritoshi, Ryosuke Tanno, Tomonori Izumitani. 2019 (modified: 09 Nov 2022); CVPR ...