Do ImageNet Classifiers Generalize?

This lecture explores the generalization of ImageNet classifiers, analyzing the progress over the past decade, safety-critical applications of machine learning, and the implications for evaluating machine learning models. It delves into the creation process of ImageNet, the three forms of overfitting, and the reliability of current machine learning models. The lecture also discusses the absence of overfitting in CIFAR-10 and ImageNet datasets, the robustness spectrum, and the future work needed to enhance robustness in machine learning. Various experiments and evaluations are presented, shedding light on the challenges and opportunities in the field of machine learning.