Translations:Batch Normalization Accelerating Deep Network Training/20/en

• A batch-normalized network matched the accuracy of the original Inception model in only 7% of the training steps (14x acceleration).
• BN-Inception (with batch normalization and other modifications) achieved a top-5 validation error of 4.82%, exceeding the accuracy of the original GoogLeNet (6.67%) and approaching human performance.
• Using batch normalization allowed training with a learning rate 10x higher than the baseline without divergence.
• On some configurations, batch normalization eliminated the need for dropout without accuracy loss, simplifying the architecture and reducing training time further.