Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data
This work addresses the theoretical understanding of why overfitting neural networks can still perform well, specifically for non-linear XOR problems with noise, which is incremental as it extends prior analyses beyond linear classifiers.
The paper tackles the problem of benign overfitting in over-parameterized neural networks by analyzing XOR-type classification tasks with label-flipping noise, showing that a ReLU CNN trained by gradient descent can achieve near Bayes-optimal accuracy under specific conditions on sample complexity and signal-to-noise ratio, with a matching lower bound when conditions are not met.
Modern deep learning models are usually highly over-parameterized so that they can overfit the training data. Surprisingly, such overfitting neural networks can usually still achieve high prediction accuracy. To study this "benign overfitting" phenomenon, a line of recent works has theoretically studied the learning of linear models and two-layer neural networks. However, most of these analyses are still limited to the very simple learning problems where the Bayes-optimal classifier is linear. In this work, we investigate a class of XOR-type classification tasks with label-flipping noises. We show that, under a certain condition on the sample complexity and signal-to-noise ratio, an over-parameterized ReLU CNN trained by gradient descent can achieve near Bayes-optimal accuracy. Moreover, we also establish a matching lower bound result showing that when the previous condition is not satisfied, the prediction accuracy of the obtained CNN is an absolute constant away from the Bayes-optimal rate. Our result demonstrates that CNNs have a remarkable capacity to efficiently learn XOR problems, even in the presence of highly correlated features.