Captum · Model Interpretability for PyTorch Introduction

Using Captum offers several benefits for machine learning practitioners. It enhances model transparency and interpretability, which is crucial for building trust in AI systems, especially in critical domains. The library helps in debugging and improving models by identifying which features are most important for predictions. This can lead to more robust and reliable models. For researchers, Captum provides a unified framework to implement and benchmark new interpretability algorithms. Its integration with PyTorch makes it easy to use with existing deep learning workflows. Additionally, Captum's multi-modal support allows for consistent interpretability approaches across different types of data and models, streamlining the development and analysis process for complex AI systems.