Captum: A unified and generic model interpretability library for PyTorch

TLDR

The paper introduces Captum, a unified, open‑source PyTorch library for model interpretability that emphasizes multimodality, extensibility, ease of use, a high‑level overview of attribution algorithms, memory‑efficient scalable computations, and an interactive visualization tool, Captum Insights, for sample‑based debugging and visualization. Captum implements generic gradient and perturbation‑based attribution algorithms, evaluation metrics, and supports classification, non‑classification, and graph‑structured neural networks, with multimodal input handling, extensibility for new algorithms, and includes an interactive visualization tool for debugging and visualizing feature importance.

Abstract

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