Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Image Classification||ImageNet||Inception V2||Top 1 Accuracy||74.8%||# 12|
|Image Classification||ImageNet||Inception V2||Top 5 Accuracy||92.2%||# 12|