RobustLearn is a unified library for research on robust machine learning, addressing adversarial attacks/defenses, out-of-distribution (OOD) generalization, and safe transfer learning. It targets ML researchers and engineers seeking to build more reliable and responsible AI systems, offering a collection of state-of-the-art methods and benchmarks.

How It Works

The library provides a modular framework for implementing and evaluating various robustness techniques. It integrates multiple research projects, each focusing on specific aspects of robust ML, such as SpecFormer for Vision Transformer robustness or NMtune for mitigating label noise. This approach allows researchers to easily compare different methods and build upon existing work.

Quick Start & Requirements

• Installation: Clone the repository and navigate to the specific project folder of interest.
• Prerequisites: Python, PyTorch. Specific projects may have additional dependencies detailed in their respective sub-folders.
• Resources: Requires standard ML development environment; specific models may need GPU acceleration.
• Links: SpecFormer Code, NMtune Code, RiFT Code, FedCLIP Code, ChatGPT Robustness Code

Highlighted Details

• Comprehensive coverage of adversarial robustness, OOD generalization, and long-tail learning.
• Includes implementations for recent research papers (ECCV 2024, ICLR 2024, ICCV 2023, KDD 2023).
• Features methods for large models like CLIP and ChatGPT, focusing on their robustness.
• Offers a unified benchmark for comparing diverse robustness techniques.

Maintenance & Community

• Developed by Microsoft.
• Welcomes contributions via pull requests, requiring agreement to a Contributor License Agreement (CLA).
• Follows the Microsoft Open Source Code of Conduct.

Licensing & Compatibility

• License: Not explicitly stated in the README, but typically Microsoft open-source projects use permissive licenses like MIT or Apache 2.0. Compatibility for commercial use is likely, but requires verification of the specific license file.

Limitations & Caveats

• The library is research-oriented, meaning some components might be experimental or subject to change.
• Specific project dependencies and setup instructions are distributed across sub-folders, requiring careful navigation.