Towards Better Robust Generalization with Shift Consistency Regularization
Authors: Shufei Zhang, Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Rui Zhang, Xinping Yi
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted to demonstrate the effectiveness of our proposed SCR method over a variety of datasets, e.g., CIFAR-10, CIFAR-100, and SVHN. It shows our method could be able to improve the robustness over the recent state-of-the-art methods substantially. |
| Researcher Affiliation | Academia | 1School of Advanced Technology, Xi an Jiaotong-Liverpool University, China. 2School of Electrical Engineering, Electronics and Computer Science, University of Liverpool, UK. 3School of Science, Xi an Jiaotong Liverpool University, China. |
| Pseudocode | Yes | Algorithm 1 Adversarial Training with SCR |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | Extensive experiments have been conducted to demonstrate the effectiveness of our proposed SCR method over a variety of datasets, e.g., CIFAR-10, CIFAR-100, and SVHN. |
| Dataset Splits | No | The paper refers to "training" and "test" datasets, for example: "The accuracy gap difference using |Acc(Sadv t , Yt) Acc(Sadv d , Yd)| |Acc(St, Yt) Acc(Sd, Yd)|, where St is the test set, Yt denotes the set of the corresponding labels of test samples in St, Sadv t is the set of adversarial examples of the test set St, and Acc(Sadv t , Yt) is the accuracy of test adversarial data set Sadv t with labels Yt.", but does not explicitly provide details about a validation split. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or cloud computing resources) used for the experiments. |
| Software Dependencies | No | The paper mentions software components like "SGD" and "Wide ResNet-28-10" (as a model architecture), but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Specifically, for FS, we follow (Zhang & Wang, 2019) on CIFAR-10 and CIFAR-100. We train 200 epochs using SGD with momentum 0.9, weight decay 5 10 4, and initial learning rate 0.1. The learning rate decays at epoch 60 and 90 with the rate 0.1; for SVHN, the initial learning rate is 0.01 while the other settings remain the same as those on CIFAR-10 and CIFAR-100. For AT and TRADES, the total training epoch is 100 with the initial learning rate 0.1 for CIFAR-10 and CIFAR-100 and 0.01 for SVHN. The learning rate decays at 60 epoch with decay rate 0.1. The trade-off parameter is 0.01 on CIFAR-10 and SVHN and 0.0001 on CIFAR-100 for our proposed model. The iteration number is empirically set as 3 to compute the regularization and update step is 4/255. All other hyperparameters are the same as the baseline methods. |