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Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
Various attribution methods have been developed to explain deep neural networks (DNNs) by inferring the attribution/importance/contribution score of each input variable to the final output.
A General Taylor Framework for Unifying and Revisiting Attribution Methods
However, the attribution problem has not been well-defined, which lacks a unified guideline to the contribution assignment process.
Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
The basic idea is to learn a source signal by back-propagation such that the mutual information between input and output should be as much as possible preserved in the mutual information between input and the source signal.
A Unified Taylor Framework for Revisiting Attribution Methods
Attribution methods have been developed to understand the decision-making process of machine learning models, especially deep neural networks, by assigning importance scores to individual features.
