Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Authors: Sergey Ioffe
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate Batch Renormalization, we applied it to the problem of image classification. Our baseline model is Inception v3 [13], trained on 1000 classes from Image Net training set [9], and evaluated on the Image Net validation data. |
| Researcher Affiliation | Industry | Sergey Ioffe Google EMAIL |
| Pseudocode | Yes | Algorithm 1: Training (top) and inference (bottom) with Batch Renormalization, applied to activation x over a mini-batch. |
| Open Source Code | No | The paper does not provide any explicit links to open-source code for the described methodology or state that the code is being released. |
| Open Datasets | Yes | Our baseline model is Inception v3 [13], trained on 1000 classes from Image Net training set [9], and evaluated on the Image Net validation data. |
| Dataset Splits | Yes | Our baseline model is Inception v3 [13], trained on 1000 classes from Image Net training set [9], and evaluated on the Image Net validation data. |
| Hardware Specification | No | The paper mentions 'The training used 50 synchronized workers [3]' but does not provide specific details about the hardware used (e.g., GPU models, CPU types, memory). |
| Software Dependencies | No | The paper mentions the use of 'RMSProp optimizer [14]' and 'Re LU [8]' which are techniques, but it does not specify any software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | The training used 50 synchronized workers [3]. Each worker processed a minibatch of 32 examples per training step. [...] For Batch Renorm, we used rmax = 1, dmax = 0 (i.e. simply batchnorm) for the first 5000 training steps, after which these were gradually relaxed to reach rmax = 3 at 40k steps, and dmax = 5 at 25k steps. [...] we used relatively fast updates to the moving statistics µ and σ, with α = 0.01. |