Sandwich Batch Normalization

Sandwich Batch Normalization: An Easy Improvement of Batch Normalization

If you are into machine learning, then you are probably familiar with Batch Normalization (BN). However, have you ever heard of Sandwich Batch Normalization (SaBN)? SaBN is a recently developed method that aims to address the inherent feature distribution heterogeneity observed in various tasks that can arise from data or model heterogeneity. With SaBN, you can easily improve the performance of your models with just a few lines of extra code.

What is SaBN?

SaBN is a method that factorizes the BN affine layer into one shared sandwich affine layer that is then followed by several parallel independent affine layers. The sandwich affine layer is shared by all channels, while the independent affine layers are executed separately for each channel. This makes SaBN a drop-in replacement in various tasks, including conditional image generation, neural architecture search (NAS), adversarial training, and arbitrary style transfer.

The Motivation Behind SaBN

The motivation behind SaBN is to address the feature distribution heterogeneity found in various machine learning tasks. This heterogeneity can occur due to data heterogeneity, where there are multiple input domains, or model heterogeneity, which could be as a result of dynamic architectures or model conditioning. These factors can cause the feature distribution to be biased, which ultimately leads to poor performance in the final model that is trained on such data.

Benefits of SaBN

One of the main benefits of SaBN is that it is an easy improvement of BN, with just a few lines of extra code. SaBN has also proved to be effective as a drop-in replacement in various tasks such as conditional image generation, neural architecture search, adversarial training, and arbitrary style transfer. SaBN has been shown to immediately achieve better results in terms of Inception score and FID on CIFAR-10 and ImageNet conditional image generation with three state-of-the-art GANs. Also, it significantly improves the performance of a state-of-the-art weight-sharing NAS algorithm on NAS-Bench-201. SaBN produces superior arbitrary stylized results and improves the robust and standard accuracies for adversarial defense.

SaBN is a promising method that addresses feature distribution heterogeneity in various machine learning tasks. SaBN is an easy improvement of BN that has proved to be effective as a drop-in replacement in various tasks. Several studies have shown SaBN to improve the performance of models with significant results. Thus, SaBN is a method that can improve the efficiency and effectiveness of machine learning models with just a few lines of extra code.

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