A primer on deep learning interpretability: ISM, DeepLIFT, DeepSHAP and integrated gradients
anshulkundaje · x · 2026-10-21
A shared primer on deep learning model interpretability, presented by Valeh Amiri and Ruchir Rastogi with illustrations. It covers the main attribution/interpretation methods — ISM, grad × input, DeepLIFT, DeepSHAP, and integrated gradients — explaining what each reveals about what a model has learned, aimed at readers getting started with interpretability.
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