Non-Robust Models
Non-robust models are machine learning or statistical models that perform well on training or test data but fail to generalize effectively to new, unseen, or slightly perturbed data. They are often characterized by high sensitivity to small changes in input, leading to poor performance in real-world scenarios where data may differ from the training distribution. This concept is critical in fields like artificial intelligence, data science, and cybersecurity, where model reliability is essential.
Developers should learn about non-robust models to avoid deploying unreliable systems in production, such as in autonomous vehicles, fraud detection, or medical diagnostics, where failures can have serious consequences. Understanding this helps in designing robust models that handle adversarial attacks, data drift, and out-of-distribution samples, ensuring better performance and trustworthiness in applications like natural language processing or computer vision.