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In this paper we introduce a novel, unified, open-source modelinterpretability library for PyTorch [12]. The library contains genericimplementations of a number of gradient and perturbation-based attributionalgorithms, also known as feature, neuron and layer importance algorithms, aswell as a set of evaluation metrics for these algorithms. It can be used forboth classification and non-classification models including graph-structuredmodels built on Neural Networks (NN). In this paper we give a high-leveloverview of supported attribution algorithms and show how to performmemory-efficient and scalable computations. We emphasize that the three maincharacteristics of the library are multimodality, extensibility and ease ofuse. Multimodality supports different modality of inputs such as image, text,audio or video. Extensibility allows adding new algorithms and features. Thelibrary is also designed for easy understanding and use. Besides, we alsointroduce an interactive visualization tool called Captum Insights that isbuilt on top of Captum library and allows sample-based model debugging andvisualization using feature importance metrics.

Captum: A unified and generic model interpretability library for PyTorch