# Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models Source: [Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models]() Collection index: [llms.txt]() Data updated: 2025-10-27T05:52:51.467Z Authors: Sergey Ioffe Published: 2017-02-10T18:27:17.000Z ## Abstract Batch Normalization is quite effective at accelerating and improving the training of deep models. However, its effectiveness diminishes when the training minibatches are small, or do not consist of independent samples. We hypothesize that this is due to the dependence of model layer inputs on all the examples in the minibatch, and different activations being produced between training and inference. We propose Batch Renormalization, a simple and effective extension to ensure that the training and inference models generate the same outputs that depend on individual examples rather than the entire minibatch. Models trained with Batch Renormalization perform substantially better than batchnorm when training with small or non-i.i.d. minibatches. At the same time, Batch Renormalization retains the benefits of batchnorm such as insensitivity to initialization and training efficiency. ## 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 ImageNet (val) (P@1 and R@5)](): Image Classification; ImageNet (val); P@1: 75.85. - [Image Classification on ImageNet (val)](): Image Classification; ImageNet (val); Top-1 Acc: 78.19. ## Original sources and implementation [Original paper]() [PDF]()