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Making sense of Occlusion output

See original GitHub issue

Hi Captum-Team. I have another understanding-question, I hope I’m not bothering you too much. Can you specify (or direct me to the explicit lines of code) what exactly is the output of the occlusion attribution? In the tutorials I only find "Let us visualize the attribution, focusing on the areas with positive attribution (those that are critical for the classifier's decision):" and the original paper provides multiple analysis. Is it plainly computing the difference between whatever the network outputs (for a specified target)? This would mean that determining what exactly negative and positive values mean is dependent on the network output.

Issue Analytics

State:
Created 3 years ago
Comments:6 (3 by maintainers)

Top GitHub Comments

1reaction

bilalsalcommented, Jun 22, 2020

Apologies, I meant the plain signed difference (without any relative computation).

0reactions

hamsarajancommented, Jun 6, 2022

Is it correct to interpret that the difference in output will be the final atribution value? Why is the range of attribution values bounded between [-1, 1]?

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