Methods › General › Interpretability › CDEP
Contextual Decomposition Explanation Penalization
CDEP
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Contextual Decomposition Explanation Penalization (CDEP) is a method which leverages existing explanation techniques for neural networks in order to prevent a model from learning unwanted relationships and ultimately improve predictive accuracy. Given particular importance scores, CDEP works by allowing the user to directly penalize importances of certain features, or interactions. This forces the neural network to not only produce the correct prediction, but also the correct explanation for that prediction
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep Models 22 Mar 2023 · 1 repository · arXiv:2303.12641
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Interpretations are useful: penalizing explanations to align neural networks with prior knowledge 30 Sep 2019 · 4 repositories · arXiv:1909.13584Syntology ran 4 of 4 samples · 0 unverified
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Age Estimation | 1 |
| Decision Making | 1 |
| Deep Learning | 1 |
| Explainable Artificial Intelligence (XAI) | 1 |
| Explainable artificial intelligence | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections