Robustness Benchmark

Robustness benchmarks evaluate the performance of machine learning models under various real-world conditions, aiming to identify and mitigate vulnerabilities to noise, corruption, and distribution shifts. Current research focuses on developing benchmarks for diverse applications, including image classification, object detection, natural language processing, and reinforcement learning, often employing convolutional neural networks, transformers, and reinforcement learning algorithms. These benchmarks are crucial for advancing the reliability and safety of AI systems across various domains, particularly in safety-critical applications like autonomous driving and medical diagnosis.