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Stochastic Gradient Descent (SGD) updates Convolutional Neural Network (CNN) with a noisy gradient computed from a random batch, and each batch evenly updates the network once in an epoch. This model applies the same training effort to each batch, but it overlooks the fact that the gradient variance, induced by Sampling Bias and Intrinsic Image Difference, renders different training dynamics on batches...
In this paper the classic momentum algorithm for stochastic optimization is considered. A method is introduced that adjusts coefficients for this algorithm during its operation. The method does not depend on any preliminary knowledge of the optimization problem. In the experimental study, the method is applied to on-line learning in feed-forward neural networks, including deep auto-encoders, and outperforms...
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