A Generalization Theory of Gradient Descent for Learning Over-parameterized Deep ReLU Networks
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Empirical studies show that gradient based methods can learn deep neural networks (DNNs) with very good generalization performance in the over-parameterization regime, where DNNs can easily fit a random labeling of the training data. While a line of recent work explains in theory that gradient-based methods with proper random initialization can find the global minima of the training loss in over-parameterized DNNs, it does not explain the good generalization performance of the gradient-based methods for learning over-parameterized DNNs. In this work, we take a step further, and prove that under certain assumption on the data distribution that is milder than linear separability, gradient descent (GD) with proper random initialization is able to train a sufficiently over-parameterized DNN to achieve arbitrarily small expected error (i.e., population error). This leads to a non-vacuous algorithmic-dependent generalization error bound for deep learning. To the best of our knowledge, this is the first result of its kind that explains the good generalization performance of over-parameterized deep neural networks learned by gradient descent.
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