InterpretDL is a comprehensive toolkit for interpreting deep learning models built with PaddlePaddle. It offers a wide array of classical and state-of-the-art interpretation algorithms, catering to researchers and practitioners seeking to understand model behavior, debug issues, and validate explanations. The library aims to simplify the process of applying and comparing various interpretability methods.

How It Works

InterpretDL provides a unified interface for various interpretation techniques, abstracting away the complexities of each algorithm. It supports model-agnostic methods like LIME and differentiable methods such as Integrated Gradients and Grad-CAM. The library is structured to allow easy integration of new algorithms and includes methods for evaluating the trustworthiness of explanations, such as perturbation tests and infidelity measures.

Quick Start & Requirements

• Installation: pip install interpretdl
• Prerequisites: Requires the paddlepaddle deep learning framework. CUDA support is recommended.
• Documentation: interpretdl.readthedocs.io
• Getting Started: A quick tutorial is available, requiring only a few minutes to become familiar with the library.

Highlighted Details

• Implements a broad taxonomy of interpretation methods, categorized by explanation representation and target model type, including specific support for Transformers.
• Includes trustworthiness evaluation algorithms like Perturbation Tests, Deletion & Insertion, and Infidelity.
• Features implementations for recent research, including papers accepted by ICML'24, Neurips'23, and TMLR.
• Provides examples and tutorials for both Computer Vision (CV) and Natural Language Processing (NLP) tasks.

Maintenance & Community

The project is actively developed, with recent updates and research paper integrations. Contributions are welcomed.

Licensing & Compatibility

InterpretDL is provided under the Apache-2.0 license, which permits commercial use and integration with closed-source projects.

Limitations & Caveats

Some dataset-level interpretation algorithms require a training process. The README indicates that the toolkit is "under active construction," suggesting potential for ongoing development and changes.