Effective Robustness

Effective robustness in machine learning focuses on improving a model's performance on unseen data beyond what's predicted by its in-distribution accuracy. Current research investigates factors like pre-training data diversity and decision boundary dynamics, employing techniques such as adversarial training and deep ensembles to enhance robustness. Understanding and achieving effective robustness is crucial for deploying reliable AI systems in real-world scenarios where data distributions inevitably shift, impacting the generalizability and trustworthiness of models. While deep ensembles offer practical improvements, recent work suggests that single, larger models can often achieve comparable levels of robustness and uncertainty quantification.