Population Gradients improve performance across data-sets and architectures in object classification

no code implementations • 23 Oct 2020 • Yurika Sakai, Andrey Kormilitzin, Qiang Liu, Alejo Nevado-Holgado

The most successful methods such as ReLU transfer functions, batch normalization, Xavier initialization, dropout, learning rate decay, or dynamic optimizers, have become standards in the field due, particularly, to their ability to increase the performance of Neural Networks (NNs) significantly and in almost all situations.

General Classification