Feb 19, 2020 · We conclude that some attribution methods are more appropriate for interpretation in terms of necessity while others are in terms of sufficiency ...
In this work we expand the foundations of human-understandable concepts with which attributions can be interpreted beyond "importance" and its visualization; we ...
Motivated by distinct, though related, criteria, a grow- ing number of attribution methods have been developed to interprete deep learning.
In this work we expand the foundations of human-understandable concepts with which attributions can be interpreted beyond importance and its visualization.
In this work we expand the foundations of human-understandable concepts with which attributions can be interpreted beyond ”importance” and its visualiza- tion; ...
This work expands the foundations of human-understandable concepts with which attributions can be interpreted beyond "importance" and its visualization; ...
This paper introduces a new way to decompose the evaluation for attribution methods into two criteria: ordering and proportionality. We argue that existing ...
For example, that the player's jerseys in basketball is a more important concept than the ball for predicting the sport in question. The work by Wang et al.
Path attribution methods are a popular tool to interpret a visual model's prediction on an input. They integrate model gradients.
In literature, several attribution methods are defined for specific machine learning models (e.g., neural networks) or more general ones that are model agnostic.
Missing: Criteria. | Show results with:Criteria.