Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data

• URL: http://arxiv.org/abs/2310.01975v1
• Date: Tue, 3 Oct 2023 11:31:37 GMT
• Title: Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data
• Authors: Xuran Meng, Difan Zou, Yuan Cao
• Abstract summary: We show that ReLU CNN trained by gradient descent can achieve near Bayes-optimal accuracy. Our result demonstrates that CNNs have a remarkable capacity to efficiently learn XOR problems, even in the presence of highly correlated features.
• Score: 24.86314525762012
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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