int_grad_corr

Integrated Gradient Correlation: a Dataset-wise Attribution Method

https://github.com/plelievre/int_grad_corr

Science Score: 67.0%

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CITATION.cff file
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Repository

Integrated Gradient Correlation: a Dataset-wise Attribution Method

Basic Info

Host: GitHub
Owner: plelievre
License: mit
Language: Python
Default Branch: main
Size: 2.06 MB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 1

Created almost 2 years ago · Last pushed 7 months ago

Metadata Files

Readme License Citation Zenodo

Integrated Gradient Correlation

Integrated Gradient Correlation (IGC) is a Python/PyTorch package that provides a unique dataset-wise attribution method.

It is designed to improve the interpretability of deep neural networks at a task-level, rather than an instance-level (as available attribution methods generally do). For more theoretical details, please refer to the original paper.

This package primarily focuses on a class computing IGC attributions for PyTorch modules. Nonetheless, it also offers utilities to calculate simple gradients, Integrated Gradients (IG), and some naive dataset-wise attribution methods.

Usage

API Reference

API Reference

Citations

Integrated Gradient Correlation: a Dataset-wise Attribution Method\ Pierre Lelièvre, Chien-Chung Chen\ Department of Psychology, National Taiwan University
Package (latest version)

License

The IGC library is freely available under the MIT License.

Owner

Login: plelievre
Kind: user

Website: plelievre.com
Repositories: 1
Profile: https://github.com/plelievre

Citation (CITATION.cff)

cff-version: 1.2.0
title: Integrated Gradient Correlation
authors:
  - affiliation: National Taiwan University
    family-names: "Lelièvre"
    given-names: Pierre
    orcid: https://orcid.org/0000-0002-2010-9105
date-released: '2025-07-10'
doi: 10.5281/zenodo.15852412
identifiers:
  - type: doi
    value: 10.5281/zenodo.15852412
type: software
license:
  - MIT
abstract: <p>Integrated Gradient Correlation (IGC) is a Python/PyTorch package that
  provides a unique dataset-wise attribution method.</p><p>It is designed to improve
  the interpretability of deep neural networks at a <i>task-level</i>, rather than
  an <i>instance-level</i> (as available attribution methods generally do). For more
  theoretical details, please refer to the original paper.</p>
keywords:
  - computer science
  - machine learning
  - deep learning
  - attribution method
  - interpretability
message: "If you use IGC in your research, please cite it using these metadata."

GitHub Events

Total

Release event: 2
Delete event: 4
Push event: 18
Pull request event: 5
Create event: 5

Last Year

Release event: 2
Delete event: 4
Push event: 18
Pull request event: 5
Create event: 5

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 0
Total pull requests: 7
Average time to close issues: N/A
Average time to close pull requests: less than a minute
Total issue authors: 0
Total pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 6
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 7
Average time to close issues: N/A
Average time to close pull requests: less than a minute
Issue authors: 0
Pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 6
Bot issues: 0
Bot pull requests: 0

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