OpenCodePapers

interpretability-techniques-for-deep-learning-1

Interpretability Techniques for Deep Learning
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PaperCodeInsertion AUC scoreModelNameReleaseDate
RISE: Randomized Input Sampling for Explanation of Black-box Models✓ Link0.5703RISE2018-06-19
Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure✓ Link0.5692HSIC-Attribution2022-06-13
A Unified Approach to Interpreting Model Predictions✓ Link0.5246Kernel SHAP2017-05-22
"Why Should I Trust You?": Explaining the Predictions of Any Classifier✓ Link0.5246LIME2016-02-16
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps✓ Link0.4632Saliency2013-12-20
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization✓ Link0.3721Grad-CAM2016-10-07
Axiomatic Attribution for Deep Networks✓ Link0.3578Integrated Gradients2017-03-04
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