albermax/innvestigate

Health v2 · maintenance only

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1309 stars · 230 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

iNNvestigate is a Python toolbox providing a common interface and out-of-the-box implementations of many neural network explanation methods such as Saliency, GuidedBackprop, SmoothGrad, IntegratedGradients, LRP, PatternNet, and PatternAttribution. It is built on TensorFlow/Keras and aims to make analyzing neural network predictions easy and comparable.

Use cases

• explain neural network predictions with saliency maps
• compute LRP relevance scores for a Keras model
• compare different XAI methods like IntegratedGradients and SmoothGrad
• visualize which pixels influenced an image classifier's decision
• interpret deep learning model decisions for research
• generate attribution maps for TensorFlow 2 models

When to choose

• you use TensorFlow/Keras and need reference implementations of many explanation algorithms under one interface
• you want to compare XAI methods like LRP, Deep Taylor, or GuidedBackprop
• you need a maintained, citable library for neural network interpretability research

When to avoid

• you work primarily in PyTorch (consider Captum instead)
• you need model-agnostic explanations for arbitrary black-box APIs
• you need explanations for non-neural-network models like gradient-boosted trees

Facets

library · maturity active

machine-learning deep-learning nlp image-processing machine-learning deep-learning artificial-intelligence data-science python xai explainability interpretability saliency-maps lrp layer-wise-relevance-propagation integrated-gradients smoothgrad tensorflow neural-network-analysis

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