Adversarial Robustness through Disentangled Representations
Authors: Shuo Yang, Tianyu Guo, Yunhe Wang, Chang Xu3145-3153
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Theoretical analysis guarantees the mitigation of the trade-off between robustness and accuracy with good disentanglement and alignment performance. Experimental results on benchmark datasets finally demonstrate the empirical superiority of our method. |
| Researcher Affiliation | Collaboration | 1School of Computer Science, Faculty of Engineering, The University of Sydney, Australia 2Key Laboratory of Machine Perception (MOE), CMIC, School of EECS, Peking University, China 3Huawei Noah s Ark Lab |
| Pseudocode | Yes | Algorithm 1: Training Process. |
| Open Source Code | Yes | We use Pytorch3 to implement our model and the code can be found here4: https://github.com/AAAI2021DRRDN/DRRDN.git |
| Open Datasets | Yes | In this paper, we use MNIST 1 and CIFAR10 2 which are benchmark datasets for the evaluation of defense methods. |
| Dataset Splits | Yes | We keep the default training/testing set splits in practice. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments. |
| Software Dependencies | No | The paper mentions "Pytorch3" but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | As for the optimization, we use the SGD optimizer with an initial learning rate of 1e 2 and 1e 1 for CIFAR10. The number of total training epoch is 100 during which a learning rate decay of 0.1 is imposed at 55, 75, 90 epochs, respectively. Also, the searching for the optimal hyper-parameters is included in the supplementary materials. |