Redundant representations help generalization in wide neural networks

Deep neural networks (DNNs) defy the classical bias-variance trade-off: adding parameters to a DNN that interpolates its training data will typically improve its generalization performance. Explaining the mechanism behind this "benign overfitting" in deep networks remains an outstanding challenge.