Captum is an open-source library developed by Facebook AI that provides interpretability and understanding of PyTorch models. It offers a suite of algorithms to help researchers and developers gain insights into the predictions made by neural networks, facilitating the identification of model behavior and potential biases. Key Features and Functionality: - Attribution Methods: Captum includes various algorithms such as Integrated Gradients, Saliency Maps, and DeepLIFT to attribute model predictions to input features. - Layer and Neuron Attribution: It allows for detailed analysis at the layer and neuron level, enabling users to understand the contribution of specific components within the network. - Feature Visualization: The library provides tools to visualize feature importance, aiding in the interpretation of complex models. - Model-Agnostic: Captum is designed to work seamlessly with any PyTorch model, ensuring flexibility across different architectures. Primary Value and User Solutions: Captum addresses the challenge of model interpretability in deep learning by offering tools that elucidate how models arrive at their predictions. This transparency is crucial for building trust in AI systems, debugging models, and ensuring compliance with regulatory standards. By providing detailed insights into model behavior, Captum empowers users to develop more reliable and accountable AI applications.