New Interpretations of Normalization Methods in Deep Learning
Authors: Jiacheng Sun, Xiangyong Cao, Hanwen Liang, Weiran Huang, Zewei Chen, Zhenguo Li5875-5882
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, a series of experiments are conducted to verify these claims.In this section, we conduct a series of experiments to verify the claims of normalization methods induced by our proposed analysis tools. |
| Researcher Affiliation | Collaboration | 1Huawei Noah s Ark Lab, 2Xi an Jiaotong University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The experiments are conducted on CIFAR-10 or CIFAR-100 dataset where images are normalized to zero mean and unit variance. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits. It mentions "CIFAR-10 or CIFAR-100 dataset" and "training samples" but no percentages or specific counts for splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Specifically, in this experiment, we train Res Net-101 model on CIFAR-10 using the SGD algorithm with learning rate 10 3 and epoch number 200. |