1 citations · 1 across the 4 of their papers we have counts for
4 papers
Visual Explanations from Deep Networks via Riemann-Stieltjes Integrated Gradient-based Localization
Mirtha Lucas, Miguel Lerma, Jacob Furst +1
Neural networks are becoming increasingly better at tasks that involve classifying and recognizing images. At the same time techniques intended to explain the network output have b…
Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients
Miguel Lerma, Mirtha Lucas
The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the output of a classifier deep network. The algorithm is simple and widely used for loc…
Baseline Computation for Attribution Methods Based on Interpolated Inputs
Miguel Lerma, Mirtha Lucas
We discuss a way to find a well behaved baseline for attribution methods that work by feeding a neural network with a sequence of interpolated inputs between two given inputs. Then…
Symmetry-Preserving Paths in Integrated Gradients
Miguel Lerma, Mirtha Lucas
We provide rigorous proofs that the Integrated Gradients (IG) attribution method for deep networks satisfies completeness and symmetry-preserving properties. We also study the uniq…