Deep Gradient Boosting -- Layer-wise Input Normalization of Neural Networks

TL;DR

This paper introduces deep gradient boosting (DGB), a novel approach that normalizes layer inputs via a boosting-inspired method, improving neural network performance without additional learnable parameters.

Contribution

It proposes a new normalization technique called input normalization layer (INN) based on gradient boosting principles, enhancing deep neural network training.

Findings

INN improves image recognition accuracy on CIFAR10 and ImageNet.

INN matches batch normalization performance without learnable parameters.

DGB offers an alternative perspective on gradient updates as normalization procedures.

Abstract

Stochastic gradient descent (SGD) has been the dominant optimization method for training deep neural networks due to its many desirable properties. One of the more remarkable and least understood quality of SGD is that it generalizes relatively well on unseen data even when the neural network has millions of parameters. We hypothesize that in certain cases it is desirable to relax its intrinsic generalization properties and introduce an extension of SGD called deep gradient boosting (DGB). The key idea of DGB is that back-propagated gradients inferred using the chain rule can be viewed as pseudo-residual targets of a gradient boosting problem. Thus at each layer of a neural network the weight update is calculated by solving the corresponding boosting problem using a linear base learner. The resulting weight update formula can also be viewed as a normalization procedure of the data that arrives at each layer during the forward pass. When implemented as a separate input normalization layer (INN) the new architecture shows improved performance on image recognition tasks when compared to the same architecture without normalization layers. As opposed to batch normalization (BN), INN has no learnable parameters however it matches its performance on CIFAR10 and ImageNet classification tasks.