Introduction
Benign overfitting is a phenomenon where a machine learning model can achieve good generalization performance, even when it perfectly fits the noisy training data. This contradicts the traditional wisdom that overfitting leads to poor generalization. Benign overfitting has been observed in various settings, such as linear regression, neural network classifiers, and convolutional neural networks. Some possible explanations for benign overfitting are the implicit regularization effects of optimization algorithms, the low effective dimensionality of the data, and the robustness of the loss function to noise. Benign overfitting challenges some of the existing theories and practices of machine learning, and motivates new research directions.