Optimal Approximation and Learning Rates for Deep Convolutional Neural Networks

08/07/2023
by   Shao-Bo Lin, et al.
0

This paper focuses on approximation and learning performance analysis for deep convolutional neural networks with zero-padding and max-pooling. We prove that, to approximate r-smooth function, the approximation rates of deep convolutional neural networks with depth L are of order (L^2/log L)^-2r/d, which is optimal up to a logarithmic factor. Furthermore, we deduce almost optimal learning rates for implementing empirical risk minimization over deep convolutional neural networks.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset