# Diminishing Batch Normalization

## TL;DR

This paper introduces Diminishing Batch Normalization (DBN), a variant of BN that updates parameters with diminishing weights, providing convergence analysis and demonstrating improved performance on modern CNNs.

## Contribution

The paper proposes a novel DBN algorithm with a diminishing averaging scheme and provides the first convergence analysis for BN variants.

## Key findings

- DBN converges to a stationary point under certain conditions.
- DBN outperforms original BN on MNIST, NI, and CIFAR-10 datasets.
- Analysis applies to models with arbitrary activation functions.

## Abstract

In this paper, we propose a generalization of the Batch Normalization (BN) algorithm, diminishing batch normalization (DBN), where we update the BN parameters in a diminishing moving average way. 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. In the numerical experiments, we test the proposed algorithm on complex modern CNN models with stochastic gradients and ReLU activation. We observe that DBN outperforms the original BN algorithm on MNIST, NI and CIFAR-10 datasets with reasonable complex FNN and CNN models.