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Captum

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PlanBilled and limitsPrice
CaptumOpen-source library · Installation via conda or pipFree

As captum.ai lists them, read 14 Nov 2026. The maker's own page has the final word.

Facts from Captum's own pages

Algorithms and metrics
Beyond attribution methods, Captum includes infidelity and sensitivity metrics to estimate explanation trustworthiness. · captum.ai, 14 Nov 2026
Attribution algorithms
Its algorithms help identify features contributing to a model’s output, including Integrated Gradients. · captum.ai, 14 Nov 2026
DeepLIFT limitation
The documented DeepLIFT implementation supports the Rescale rule; RevealCancel is not supported in the described implementation. · captum.ai, 14 Nov 2026
Distribution
The site documents installation using either conda or pip. · captum.ai, 14 Nov 2026
Maintainer
The site footer identifies Meta Platforms, Inc. in its copyright notice. · captum.ai, 14 Nov 2026
Model compatibility caveat
Some hook-based methods do not work with functional nonlinearities; JIT models do not support hook-dependent methods. · captum.ai, 14 Nov 2026
Parallel models
The FAQ states that DataParallel and DistributedDataParallel models are supported with all model types. · captum.ai, 14 Nov 2026
PyTorch models
Captum supports most types of PyTorch models and is intended to require minimal changes to the original neural network. · captum.ai, 14 Nov 2026
Research use
Researchers can implement interpretability algorithms and benchmark them against methods in the library. · captum.ai, 14 Nov 2026
Supported modalities
The product site says Captum supports interpretability across vision, text, and other modalities. · captum.ai, 14 Nov 2026
What it does
Captum is an open-source library for interpreting machine-learning models built on PyTorch. · captum.ai, 14 Nov 2026
Who it is for
The stated primary audiences are model developers and interpretability researchers; application engineers are also identified as users. · captum.ai, 14 Nov 2026

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