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Baseline Computation for Attribution Methods Based on Interpolated Inputs

arXiv 2204.06120 ↗First posted 13 Apr 2022cs.CV cs.LGcited by 0

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Abstract (arXiv)

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, we test it with our novel Riemann-Stieltjes Integrated Gradient-weighted Class Activation Mapping (RSI-Grad-CAM) attribution method.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 13 Apr 2022.

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6 of 11 references.

References · 6

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  4. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  5. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  6. 2021Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoringcited by 16

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