Practical overview
When it fits
Use Captum when you have a PyTorch model and want to inspect feature contributions, investigate an output, or compare interpretability methods.
Who it is for
- PyTorch model developers investigating important features or unexpected outputs.
- Interpretability researchers implementing and benchmarking algorithms.
- Application engineers working with trained models who want to investigate or explain outcomes.
Documented uses
- Identify input features contributing to a model output.
- Investigate unexpected model predictions to inform troubleshooting.
- Implement interpretability algorithms that interact with PyTorch models.
- Benchmark an interpretability method against algorithms available in the library.
- Develop explanations that may help users understand outcomes such as recommendations.
Requirements & limits
- Captum is a library for PyTorch models, rather than a standalone model-monitoring service.
- It supports both applying interpretability methods and benchmarking against other algorithms in the library.
- Interpretability results can inform troubleshooting or user-facing explanations, but do not by themselves establish model accuracy or make an explanation definitive.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Use Captum to investigate features contributing to a model output
Apply an interpretability algorithm to a PyTorch model to help investigate its output.
Before you start
- A PyTorch model.
Steps
Use Captum’s interpretability algorithms with the PyTorch model.
Inspect which input features contribute to the model’s output.
Use the findings to investigate unexpected outputs or improve the model.
Expected outcome
An interpretation identifying features that contribute to the model output, which can inform troubleshooting or model improvement.
Guide limitations
- The source describes this workflow but does not specify code, algorithm-selection criteria, or a procedure for interpreting attribution results.
Benchmark an interpretability algorithm against algorithms in Captum
Compare interpretability research against other algorithms available in Captum.
Before you start
- An interpretability algorithm to benchmark.
Steps
Use Captum to implement an interpretability algorithm that interacts with PyTorch models.
Benchmark the work against other existing algorithms available in the library.
Expected outcome
A benchmark of the work against existing algorithms in Captum.
Guide limitations
- The source does not describe benchmark metrics, setup details, or a step-by-step comparison procedure.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 12 October 2026
- Original website title
- Captum · Model Interpretability for PyTorch · Official homepage title · retained in its source language