Batch Layer Normalization, A new normalization layer for CNNs and RNN

• URL: http://arxiv.org/abs/2209.08898v1
• Date: Mon, 19 Sep 2022 10:12:51 GMT
• Title: Batch Layer Normalization, A new normalization layer for CNNs and RNN
• Authors: Amir Ziaee, Erion \c{C}ano
• Abstract summary: This study introduces a new normalization layer termed Batch Layer Normalization (BLN) As a combined version of batch and layer normalization, BLN adaptively puts appropriate weight on mini-batch and feature normalization based on the inverse size of mini-batches. Test results indicate the application potential of BLN and its faster convergence than batch normalization and layer normalization in both Convolutional and Recurrent Neural Networks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This study introduces a new normalization layer termed Batch Layer Normalization (BLN) to reduce the problem of internal covariate shift in deep neural network layers. As a combined version of batch and layer normalization, BLN adaptively puts appropriate weight on mini-batch and feature normalization based on the inverse size of mini-batches to normalize the input to a layer during the learning process. It also performs the exact computation with a minor change at inference times, using either mini-batch statistics or population statistics. The decision process to either use statistics of mini-batch or population gives BLN the ability to play a comprehensive role in the hyper-parameter optimization process of models. The key advantage of BLN is the support of the theoretical analysis of being independent of the input data, and its statistical configuration heavily depends on the task performed, the amount of training data, and the size of batches. Test results indicate the application potential of BLN and its faster convergence than batch normalization and layer normalization in both Convolutional and Recurrent Neural Networks. The code of the experiments is publicly available online (https://github.com/A2Amir/Batch-Layer-Normalization).

Related papers

• Exploring the Efficacy of Group-Normalization in Deep Learning Models for Alzheimer's Disease Classification [2.6447365674762273]
  Group Normalization is an easy alternative to Batch Normalization. GN achieves a very low error rate of 10.6% compared to Batch Normalization.
  arXiv  Detail & Related papers  (2024-04-01T06:10:11Z)
• Context Normalization Layer with Applications [0.1499944454332829]
  This study proposes a new normalization technique, called context normalization, for image data. It adjusts the scaling of features based on the characteristics of each sample, which improves the model's convergence speed and performance. The effectiveness of context normalization is demonstrated on various datasets, and its performance is compared to other standard normalization techniques.
  arXiv  Detail & Related papers  (2023-03-14T06:38:17Z)
• Distribution Mismatch Correction for Improved Robustness in Deep Neural Networks [86.42889611784855]
  normalization methods increase the vulnerability with respect to noise and input corruptions. We propose an unsupervised non-parametric distribution correction method that adapts the activation distribution of each layer. In our experiments, we empirically show that the proposed method effectively reduces the impact of intense image corruptions.
  arXiv  Detail & Related papers  (2021-10-05T11:36:25Z)
• Comparing Normalization Methods for Limited Batch Size Segmentation Neural Networks [0.0]
  Batch Normalization works best using large batch size during training. We show the effectiveness of Instance Normalization in the limited batch size neural network training environment. We also show that the Instance Normalization implementation used in this experiment is computational time efficient when compared to the network without any normalization method.
  arXiv  Detail & Related papers  (2020-11-23T17:13:24Z)
• MimicNorm: Weight Mean and Last BN Layer Mimic the Dynamic of Batch Normalization [60.36100335878855]
  We propose a novel normalization method, named MimicNorm, to improve the convergence and efficiency in network training. We leverage the neural kernel (NTK) theory to prove that our weight mean operation whitens activations and transits network into the chaotic regime like BN layer. MimicNorm achieves similar accuracy for various network structures, including ResNets and lightweight networks like ShuffleNet, with a reduction of about 20% memory consumption.
  arXiv  Detail & Related papers  (2020-10-19T07:42:41Z)
• Double Forward Propagation for Memorized Batch Normalization [68.34268180871416]
  Batch Normalization (BN) has been a standard component in designing deep neural networks (DNNs) We propose a memorized batch normalization (MBN) which considers multiple recent batches to obtain more accurate and robust statistics. Compared to related methods, the proposed MBN exhibits consistent behaviors in both training and inference.
  arXiv  Detail & Related papers  (2020-10-10T08:48:41Z)
• Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction [3.201333208812837]
  We propose a new and effective normalization approaches based on LayerNorm named variance only LayerNorm(VO-LN) in this work. We find that the variance of normalization plays the main role and give an explanation in this work.
  arXiv  Detail & Related papers  (2020-06-23T04:35:22Z)
• PowerNorm: Rethinking Batch Normalization in Transformers [96.14956636022957]
  normalization method for neural network (NN) models used in Natural Language Processing (NLP) is layer normalization (LN) LN is preferred due to the empirical observation that a (naive/vanilla) use of BN leads to significant performance degradation for NLP tasks. We propose Power Normalization (PN), a novel normalization scheme that resolves this issue.
  arXiv  Detail & Related papers  (2020-03-17T17:50:26Z)
• Cross-Iteration Batch Normalization [67.83430009388678]
  We present Cross-It Batch Normalization (CBN), in which examples from multiple recent iterations are jointly utilized to enhance estimation quality. CBN is found to outperform the original batch normalization and a direct calculation of statistics over previous iterations without the proposed compensation technique.
  arXiv  Detail & Related papers  (2020-02-13T18:52:57Z)
• Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization [126.6252371899064]
  Moving Average Batch Normalization (MABN) is a novel normalization method. We show that MABN can completely restore the performance of vanilla BN in small batch cases. Our experiments demonstrate the effectiveness of MABN in multiple computer vision tasks including ImageNet and COCO.
  arXiv  Detail & Related papers  (2020-01-19T14:41:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.