Captum is an open-source model interpretability library designed for the PyTorch framework, primarily to help users understand the basis of predictions and feature importance in deep learning models.
It supports multiple attribution methods, including Integrated Gradients, saliency maps, DeepLIFT, feature ablation, as well as internal attribution analyses such as layer attribution and neuron attribution.
Requires Python 3.6+ and PyTorch 1.2+ with a PyTorch model ready for interpretation.
Primarily aimed at model developers, explainability researchers, and application engineers who need to debug and explain models in production.
Yes. Captum is designed to support multimodal data and can be applied to models that explain across visual, text, and other data modalities.
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