Transformer-MM-Explainability
AIX360
| Transformer-MM-Explainability | AIX360 | |
|---|---|---|
| 3 | 2 | |
| 709 | 1,533 | |
| - | 2.0% | |
| 0.0 | 8.2 | |
| 8 months ago | 2 months ago | |
| Jupyter Notebook | Python | |
| MIT License | Apache License 2.0 |
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Transformer-MM-Explainability
AIX360
- [D] DL Practitioners, Do You Use Layer Visualization Tools s.a GradCam in Your Process?
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[R] Explaining the Explainable AI: A 2-Stage Approach - Link to a free online lecture by the author in comments
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques https://arxiv.org/abs/1909.03012 https://github.com/Trusted-AI/AIX360
What are some alternatives?
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shap - A game theoretic approach to explain the output of any machine learning model.
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
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awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
pytea - PyTea: PyTorch Tensor shape error analyzer
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