Captum · Model Interpretability for PyTorch

Captum, meaning 'comprehension' in Latin, is a model interpretability and understanding library built on PyTorch. It offers a wide range of attribution algorithms and visualization tools to help researchers and developers understand how their PyTorch models make predictions. Captum supports interpretability across various modalities including vision, text, and more, making it versatile for different types of deep learning applications. The library is designed to work with most PyTorch models with minimal modifications to the original neural network architecture.

Highlights:

• Open-source model interpretability library for PyTorch
• Supports multiple modalities including vision and text
• Offers an extensible framework for new interpretability algorithms
• Provides comprehensive attribution methods and visualization tools
• Designed to work with most PyTorch models with minimal modifications

Key Features:

• Multi-Modal Support
• PyTorch Integration
• Extensible Framework
• Comprehensive Attribution Methods
• Visualization Tools

Benefits:

• Improves model performance by understanding feature contributions
• Aids in debugging and refining deep learning models
• Helps ensure model fairness by identifying and mitigating biases
• Enhances explainable AI in healthcare and other industries
• Increases transparency and trust in AI model decisions

Use Cases:

• Improving Model Performance
• Debugging Deep Learning Models
• Ensuring Model Fairness
• Enhancing Explainable AI in Healthcare
• Increasing Trust and Transparency in AI Models