Ghost Noise for Regularizing Deep Neural Networks

Authors

  • Atli Kosson EPFL
  • Dongyang Fan EPFL
  • Martin Jaggi EPFL

DOI:

https://doi.org/10.1609/aaai.v38i12.29228

Keywords:

ML: Deep Learning Algorithms, ML: Optimization

Abstract

Batch Normalization (BN) is widely used to stabilize the optimization process and improve the test performance of deep neural networks. The regularization effect of BN depends on the batch size and explicitly using smaller batch sizes with Batch Normalization, a method known as Ghost Batch Normalization (GBN), has been found to improve generalization in many settings. We investigate the effectiveness of GBN by disentangling the induced ``Ghost Noise'' from normalization and quantitatively analyzing the distribution of noise as well as its impact on model performance. Inspired by our analysis, we propose a new regularization technique called Ghost Noise Injection (GNI) that imitates the noise in GBN without incurring the detrimental train-test discrepancy effects of small batch training. We experimentally show that GNI can provide a greater generalization benefit than GBN. Ghost Noise Injection can also be beneficial in otherwise non-noisy settings such as layer-normalized networks, providing additional evidence of the usefulness of Ghost Noise in Batch Normalization as a regularizer.

Published

2024-03-24

How to Cite

Kosson, A., Fan, D., & Jaggi, M. (2024). Ghost Noise for Regularizing Deep Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 38(12), 13274-13282. https://doi.org/10.1609/aaai.v38i12.29228

Issue

Section

AAAI Technical Track on Machine Learning III