Removing Batch Normalization Boosts Adversarial Training
Authors: Haotao Wang, Aston Zhang, Shuai Zheng, Xingjian Shi, Mu Li, Zhangyang Wang
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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. |