Captum

What is Captum?

Captum is a comprehensive model interpretability library built on PyTorch, designed to help researchers and developers analyze which features, concepts, or training examples influence model predictions. It supports a wide range of PyTorch models and modalities including vision, text, and audio. Captum offers state-of-the-art attribution algorithms such as Integrated Gradients and concept-based methods like TCAV, along with robustness tools to detect vulnerabilities through adversarial attacks and perturbations. Its extensible design facilitates research and practical troubleshooting to improve model transparency and reliability.

Key Features

• Multi-Modal Interpretability

  Supports interpretation of models handling images, text, audio, and more, enabling broad applicability.

• Wide Range of Attribution Methods

  Includes algorithms like Integrated Gradients, Saliency Maps, TCAV, and Layer-wise Relevance Propagation for detailed feature and concept analysis.

• Robustness and Adversarial Tools

  Provides metrics and attacks such as fast-gradient sign method and projected-gradient descent to evaluate and enhance model robustness.

• Seamless PyTorch Integration

  Works with most PyTorch models with minimal modification, including those built with torchvision and torchtext.

• Open Source and Extensible

  Allows researchers to implement new interpretability algorithms and benchmark them easily within the library.

Use Cases

• Model Debugging and Improvement : Identify key features and concepts influencing predictions to refine and troubleshoot models.
• Research in Interpretability : Develop and benchmark new interpretability algorithms across diverse model types and data modalities.
• Fairness and Bias Analysis : Use concept-based methods like TCAV to detect and assess biases related to sensitive attributes in models.
• Production Model Monitoring : Enhance transparency for deployed models, aiding in troubleshooting and explaining outputs to end users.

FAQs