Bayesian Adversarial Learning
Authors: Nanyang Ye, Zhanxing Zhu
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed methods on two commonly used datasets, MNIST and CIFAR-10, and compare with various adversarial learning methods. As shown in Table 1, Bayesian adversarial training consistently achieves higher adversarial accuracy on both MNIST and CIFAR-10 on a variety of attacks. |
| Researcher Affiliation | Collaboration | Yingqian Li1, Xuanqing Liu1, Xingxing Zhang2, Zhuowen Tu1, Bo Li1. 1 University of California, San Diego. 2 Google AI |
| Pseudocode | Yes | Algorithm 1: Bayesian Adversarial Training (BAT) |
| Open Source Code | No | The paper does not provide an explicit statement about releasing the source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | We evaluate the proposed methods on two commonly used datasets, MNIST and CIFAR-10, and compare with various adversarial learning methods. |
| Dataset Splits | Yes | For MNIST, we use a 60K training set and 10K test set. For CIFAR-10, we use a 50K training set and 10K test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models or processor types) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names with version numbers like Python 3.8, PyTorch 1.9) needed to replicate the experiment. |
| Experiment Setup | Yes | For MNIST, we use a LeNet-5 architecture [22] with 2 convolutional layers and 2 fully connected layers. The training is performed for 100 epochs using Adam optimizer with a learning rate of 0.001. For CIFAR-10, we use a Wide ResNet-28-10 architecture [32]. The training is performed for 200 epochs using Adam optimizer with a learning rate of 0.001. |