Phase Transition from Clean Training to Adversarial Training
Authors: Yue Xing, Qifan Song, Guang Cheng
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate this conjecture in linear regression models, and conduct comprehensive experiments in deep neural networks. |
| Researcher Affiliation | Academia | Yue Xing Department of Statistics Purdue University xing49@purdue.edu Qifan Song Department of Statistics Purdue University qfsong@purdue.edu Guang Cheng Department of Statistics University of California, Los Angeles guangcheng@ucla.edu |
| Pseudocode | No | The paper refers to an 'algorithm' and describes steps for approximation in Section 6 and Appendix A, but it does not present a formal pseudocode or algorithm block. |
| Open Source Code | No | We provide the github links for the original code we modified from. |
| Open Datasets | Yes | For datasets CIFAR-10, CIFAR-100, SVHN, we modified the code of Rice et al. (2020) for our implementation. ... For MNIST, we implement a CNN with two convolution layers and two fully connected layers. |
| Dataset Splits | No | We use an SGD optimizer with batch size 128 to train on the full training set for 200 epochs. ... After training 200 epochs, we find the epoch with the smallest adversarial testing loss as the final model for early stopping. |
| Hardware Specification | No | The paper mentions training times (e.g., 'it takes 20 minutes to train the CIFAR-10 dataset for clean training but takes 10 hours to complete adversarial training') but does not specify the type of hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper states 'we modified the code of Rice et al. (2020) for our implementation' but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | If there is no specification, we use an SGD optimizer with batch size 128 to train on the full training set for 200 epochs. The learning rate is initialized as 0.1 for CIFAR and 0.01 for SVHN, and is divided by 10 at the 100th and 150th epochs. We consider Pre Act Res Net18 and Wide Res Net34 as those in Rice et al. (2020). |