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Title: Diminishing Batch Normalization

Paper content:
In this paper, we propose a generalization of the BN algorithm, diminishing batch normalization (DBN), where we update the BN parameters in a diminishing moving average way.
Batch normalization (BN) is very effective in accelerating the convergence of a neural network training phase that it has become a common practice.
Our proposed DBN algorithm remains the overall structure of the original BN algorithm while introduces a weighted averaging update to some trainable parameters.
We provide an analysis of the convergence of the DBN algorithm that converges to a stationary point with respect to trainable parameters.
Our analysis can be easily generalized for original BN algorithm by setting some parameters to constant.
To the best knowledge of authors, this analysis is the first of its kind for convergence with Batch Normalization introduced.
We analyze a two-layer model with arbitrary activation function.
The primary challenge of the analysis is the fact that some parameters are updated by gradient while others are not.
The convergence analysis applies to any activation function that satisfies our common assumptions.
For the analysis, we also show the sufficient and necessary conditions for the stepsizes and diminishing weights to ensure the convergence.
In the numerical experiments, we use more complex models with more layers and ReLU activation.
We observe that DBN outperforms the original BN algorithm on Imagenet, MNIST, NI and CIFAR-10 datasets with reasonable complex FNN and CNN models.
Deep neural networks (DNN) have shown unprecedented success in various applications such as object detection.
However, it still takes a long time to train a DNN until it converges.
Ioffe & Szegedy identified a critical problem involved in training deep networks, internal covariate shift, and then proposed batch normalization (BN) to decrease this phenomenon.
BN addresses this problem by normalizing the distribution of every hidden layer's input.
In order to do so, it calculates the preactivation mean and standard deviation using mini-batch statistics at each iteration of training and uses these estimates to normalize the input to the next layer.
The output of a layer is normalized by using the batch statistics, and two new trainable parameters per neuron are introduced that capture the inverse operation.
It is now a standard practice Bottou et al. (2016) ; He et al. (2016) .
While this approach leads to a significant performance jump, to the best of our knowledge, there is no known theoretical guarantee for the convergence of an algorithm with BN.
The difficulty of analyzing the convergence of the BN algorithm comes from the fact that not all of the BN parameters are updated by gradients.
Thus, it invalidates most of the classical studies of convergence for gradient methods.In this paper, we propose a generalization of the BN algorithm, diminishing batch normalization (DBN), where we update the BN parameters in a diminishing moving average way.
It essentially means that the BN layer adjusts its output according to all past mini-batches instead of only the current one.
It helps to reduce the problem of the original BN that the output of a BN layer on a particular training pattern depends on the other patterns in the current mini-batch, which is pointed out by Bottou et al..
By setting the layer parameter we introduce into DBN to a specific value, we recover the original BN algorithm.We give a convergence analysis of the algorithm with a two-layer batch-normalized neural network and diminishing stepsizes.
We assume two layers (the generalization to multiple layers can be made by using the same approach but substantially complicating the notation) and an arbitrary loss function.
The convergence analysis applies to any activation function that follows our common assumption.
The main result shows that under diminishing stepsizes on gradient updates and updates on mini-batch statistics, and standard Lipschitz conditions on loss functions DBN converges to a stationary point.
As already pointed out the primary challenge is the fact that some trainable parameters are updated by gradient while others are updated by a minor recalculation.Contributions.
The main contribution of this paper is in providing a general convergence guarantee for DBN.
Specifically, we make the following contributions.•
In section 4, we show the sufficient and necessary conditions for the stepsizes and diminishing weights to ensure the convergence of BN parameters.•
We show that the algorithm converges to a stationary point under a general nonconvex objective function.This paper is organized as follows. In
Section 2, we review the related works and the development of the BN algorithm. We
formally state our model and algorithm in Section 3.
We present our main results in Sections 4.
In Section 5, we numerically show that the DBN algorithm outperforms the original BN algorithm. Proofs
for main steps are collected in the Appendix.

assistant: text: We propose a extension of the batch normalization, show a first-of-its-kind convergence analysis for this extension and show in numerical experiments that it has better performance than the original batch normalizatin.