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Captum

swMATH ID: 35075
Software Authors: Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, Orion Reblitz-Richardson
Description: Captum: A unified and generic model interpretability library for PyTorch. 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.
Homepage: https://arxiv.org/abs/2009.07896
Dependencies: PyTorch
Keywords: Machine Learning; arXiv_cs.LG; Artificial Intelligence; arXiv_cs.AI; arXiv_stat.ML; PyTorch; Interpretability; Attribution; Multi-Modal; Model Understanding
Related Software: PyTorch; InterpretML; Python; Alibi Explain; iNNvestigate; Alibi; AI Explainability 360; GitHub; TensorFlow; DistilBERT; jiant; RoBERTa; BERT; diagNNose; SmoothGrad; Grad-CAM; Seldon Core; KFServing; Ray; tf-explain
Referenced in: 2 Publications

Standard Articles

1 Publication describing the Software Year
Captum: A unified and generic model interpretability library for PyTorch
Narine Kokhlikyan, Vivek Miglani, Miguel Martin, Edward Wang, Bilal Alsallakh, Jonathan Reynolds, Alexander Melnikov, Natalia Kliushkina, Carlos Araya, Siqi Yan, Orion Reblitz-Richardson
2020

Referenced in 1 Field

2 Computer science (68-XX)

Referencing Publications by Year