HOW MANY SAMPLES ARE NEEDED TO TRAIN A DEEP NEURAL NETWORK?
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Resumen del artículo
This paper establishes a lower bound for the number of samples needed to train a deep ReLU neural network, showing it scales at a rate of 1/√n, slower than classical methods. This theoretical result is supported by experiments on benchmark datasets for image classification and regression tasks. The findings confirm the common belief that deep learning requires a large amount of data for effective training.
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Deep learning models, like those used for image recognition, need lots of examples to learn well. This paper uses math and experiments to show they learn slower than simpler models.
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Explicación de la calificación
This paper provides a valuable theoretical and empirical analysis of sample complexity in deep learning. The derived lower bound and supporting experiments offer new insights into why deep learning models often require extensive training data. While the theoretical scope is limited to feedforward networks and assumes high input dimensions, the findings are significant and match existing upper bounds. The paper successfully addresses a fundamental question in deep learning, justifying its 4 rating.
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