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..
Removing Batch Normalization Boosts Adversarial Training
Authors: Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that No Frost achieves a better accuracy-robustness trade-off compared with previous stateof-the-art AT methods based on BN or MBN models. |
| Researcher Affiliation | Collaboration | 1University of Texas at Austin, Austin, USA 2Amazon Web Services, Santa Clara, USA. |
| Pseudocode | No | The paper describes methods in text and equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and pretrained models are public1. 1https://github.com/amazon-research/normalizer-free-robust-training |
| Open Datasets | Yes | All methods are trained on the Image Net (Deng et al., 2009) dataset. |
| Dataset Splits | Yes | We evaluate clean accuracy on the Image Net validation set, and use the accuracy on adversarial test images as a metric for adversarial robustness. |
| Hardware Specification | Yes | All experiments are conducted with 8 NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions optimizers (SGD) but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA versions). |
| Experiment Setup | Yes | For all experiments, we train on Image Net for 90 epochs. We use the SGD optimizer with momentum 0.9. Batch size is 256. Weight decay factor is 5e-5. The initial learning rate is 0.1 and decays following a cosine annealing scheduler. |