Captum (“comprehension” in Latin) is an open source, extensible library for model interpretability built on PyTorch.
With the increase in model complexity and the resulting lack of transparency, model interpretability methods have become increasingly important. Model understanding is both an active area of research as well as an area of focus for practical applications across industries using machine learning. Captum provides state-of-the-art algorithms, including Integrated Gradients, to provide researchers and developers with an easy way to understand which features are contributing to a model’s output.
Captum helps ML researchers more easily implement interpretability algorithms that can interact with PyTorch models. It also allows researchers to quickly benchmark their work against other existing algorithms available in the library.
For model developers, Captum can be used to improve and troubleshoot models by facilitating the identification of different features that contribute to a model’s output in order to design better models and troubleshoot unexpected model outputs.
Multi-Modal: Supports interpretability of models across modalities including vision, text, and more.
Built on PyTorch: Supports most types of PyTorch models and can be used with minimal modification to the original neural network.
Extensible: Open source, generic library for interpretability research. Easily implement and benchmark new algorithms.