easy_explain
AIX360
easy_explain | AIX360 | |
---|---|---|
1 | 2 | |
9 | 1,543 | |
- | 2.7% | |
6.5 | 8.2 | |
2 months ago | 3 months ago | |
Python | Python | |
MIT License | Apache License 2.0 |
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easy_explain
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Do you want an easy and quick way to explain your image models?
Find the package in Gh: https://github.com/stavrostheocharis/easy_explain
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?
tf-keras-vis - Neural network visualization toolkit for tf.keras
AIF360 - A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.
explainable-cnn - 📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.
cleverhans - An adversarial example library for constructing attacks, building defenses, and benchmarking both
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
backpack - BackPACK - a backpropagation package built on top of PyTorch which efficiently computes quantities other than the gradient.
DALEX - moDel Agnostic Language for Exploration and eXplanation
TorchDrift - Drift Detection for your PyTorch Models
pytea - PyTea: PyTorch Tensor shape error analyzer
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
shapley - The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).