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..
Random Normalization Aggregation for Adversarial Defense
Authors: Minjing Dong, Xinghao Chen, Yunhe Wang, Chang Xu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on various models and datasets, and demonstrate the strong superiority of proposed algorithm.In this section, we provide sufficient evaluation of RNA module on various models and datasets. |
| Researcher Affiliation | Collaboration | Minjing Dong1, Xinghao Chen2, Yunhe Wang2, Chang Xu1 1School of Computer Science, University of Sydney 2Huawei Noah s Ark Lab EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Random Normalization Aggregation with Black-box Adversarial Training |
| Open Source Code | Yes | The Py Torch code is available at https://github.com/Uni Serj/ Random-Norm-Aggregation and the Mind Spore code is available at https: //gitee.com/mindspore/models/tree/master/research/cv/RNA. |
| Open Datasets | Yes | CIFAR-10/100 We first conduct experiments on CIFAR-10/100 [31] datasets, which contain 50K training images and 10K testing images with size of 32 32 from 10/100 categories. The networks we use are Res Net-18 [31] and Wide Res Net-32 (WRN) [32].The effectiveness of proposed RNA is also evaluated on Image Net [35], which contains 1.2M training images and 50K testing images with size of 224 224 from 1000 categories. |
| Dataset Splits | No | The paper describes training and testing image counts for CIFAR-10/100 and ImageNet, but does not explicitly provide details about a validation dataset split, specific percentages for train/val/test, or mention cross-validation. |
| Hardware Specification | Yes | The experiments are performed on one V100 GPU using Pytorch [33] and Mindspore [34].The experiments are performed on eight V100 GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch [33]' and 'Mindspore [34]' as software used but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The SGD optimizer with a momentum of 0.9 is used. The weight decay is set to 5 10 4. The initial learning rate is set to 0.1 with a piecewise decay learning rate scheduler. All the baselines are trained with 200 epochs with a batch size of 128. The PGD-10 with ϵ = 8/255 and step size of 2/255 is adopted in the adversarial training setting. The SGD optimizer with a momentum of 0.9 is used. The weight decay is set to 1 10 4. The initial learning rate is set to 0.02 with a cosine learning rate scheduler. We load a pretrained Res Net-50 and then adversarailly train the network for 60 epochs with a batch size of 512. The PGD-2 with ϵ = 4/255 is adopted in the adversarial training setting. |