# Micro-Batch Training with Batch-Channel Normalization and Weight Standardization Source: [Micro-Batch Training with Batch-Channel Normalization and Weight Standardization]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:11:17.910Z Authors: Siyuan Qiao, Huiyu Wang, Chenxi Liu, Wei Shen, Alan Yuille Published: 2019-03-25T18:00:05.000Z ## Abstract Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typically has only 1-2 images for training, which is inevitable for many computer vision tasks, e.g., object detection and semantic segmentation, constrained by memory consumption. To address this issue, we propose Weight Standardization (WS) and Batch-Channel Normalization (BCN) to bring two success factors of BN into micro-batch training: 1) the smoothing effects on the loss landscape and 2) the ability to avoid harmful elimination singularities along the training trajectory. WS standardizes the weights in convolutional layers to smooth the loss landscape by reducing the Lipschitz constants of the loss and the gradients; BCN combines batch and channel normalizations and leverages estimated statistics of the activations in convolutional layers to keep networks away from elimination singularities. We validate WS and BCN on comprehensive computer vision tasks, including image classification, object detection, instance segmentation, video recognition and semantic segmentation. All experimental results consistently show that WS and BCN improve micro-batch training significantly. Moreover, using WS and BCN with micro-batch training is even able to match or outperform the performances of BN with large-batch training. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR-10 (test) (Error Rate)](): Image Classification; CIFAR-10 (test); Error Rate: 4.96. - [Semantic Segmentation on PASCAL VOC 2012 (val)](): Semantic segmentation; PASCAL VOC 2012 (val); Mean IoU: 78.22. - [Action Recognition on Something-Something v1 (val)](): Action Recognition; Something-something v1 (val); Top-1 Acc: 46.49. - [Instance Segmentation on COCO 2017 (val)](): Instance Segmentation; COCO 2017 (val); APm: 0.391. - [Object Detection on COCO 2017 (val)](): Object Detection; COCO 2017 (val); AP: 41.8. - [Image Classification on ImageNet (val)](): Image Classification; ImageNet (val); Top-1 Acc: not recorded. ## Original sources and implementation [Original paper]() [PDF]()