Venue
International Conference on Machine Learning
Domain
Artificial Intelligence / Machine Learning / Computer Vision
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.Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch.Batch Normalization allows us to use much higher learning rates and be less careful about initialization.It also acts as a regularizer, in some cases eliminating the need for Dropout.Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin.Using an ensemble of batchnormalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.
The paper discusses Batch Normalization (BN), a technique designed to accelerate the training of deep neural networks by addressing the issue of internal covariate shift, which occurs as the distribution of each layer's inputs changes during training. By normalizing the inputs for each mini-batch, BN allows for higher learning rates and faster training while acting as a regularizer that can even replace the need for Dropout in some cases. The authors demonstrate that applying BN to a state-of-the-art image classification model can significantly reduce the number of training steps required to achieve high accuracy on tasks such as ImageNet classification, achieving a top-5 validation error of 4.9%. The mechanism of BN is also detailed, including the use of learnable parameters for scaling and shifting the normalized values, which preserves network capacity and prevents saturation in nonlinear activation functions. The approach is empirically validated through experiments on datasets like MNIST and ImageNet, showing improved performance and convergence in training deep networks.
This paper employs the following methods:
The following datasets were used in this research:
- Top-5 validation error
- Accuracy
- Achieved 4.9% top-5 validation error on ImageNet
- Matched Inception performance using only 7% of the training steps
- Eliminated the need for Dropout
The authors identified the following limitations:
- Increased number of parameters by 25% and computational cost by 30%
- Number of GPUs: None specified
- GPU Type: None specified
batch normalization
deep learning
neural networks
internal covariate shift
training acceleration