CSTAR: Towards Compact and Structured Deep Neural Networks with Adversarial Robustness
Authors: Huy Phan, Miao Yin, Yang Sui, Bo Yuan, Saman Zonouz
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations for various DNN models on different datasets demonstrate the effectiveness of CSTAR. Compared with the state-of-the-art robust structured pruning, CSTAR shows consistently better performance. For instance, when compressing Res Net-18 on CIFAR-10, CSTAR achieves up to 20.07% and 11.91% improvement for benign accuracy and robust accuracy, respectively. |
| Researcher Affiliation | Academia | Huy Phan1, Miao Yin1, Yang Sui1, Bo Yuan1, Saman Zonouz2 1Department of Electrical and Computer Engineering, Rutgers University 2Schools of Cybersecurity and Privacy, Georgia Institute of Technology |
| Pseudocode | Yes | Algorithm 1: Tucker-2 proj. for solving Eqn. 12; Algorithm 2: The overall procedure of CSTAR |
| Open Source Code | No | The paper does not provide a specific statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | The models are compressed on CIFAR-10/100 and Image Net datasets with different compression ratios ranging from 2 to 64 . |
| Dataset Splits | No | The paper does not explicitly detail the use of a separate 'validation' dataset split or its specific proportion, only mentioning 'training' and 'testing' iterations. |
| Hardware Specification | Yes | Table 6 reports the inference time with batch size = 1 for the original and compressed models on both CPU (AMD Ryzen 9 5900HX) and GPU (NVIDIA Ge Force RTX 3090). |
| Software Dependencies | No | The paper mentions 'Py Torch and Tensor Flow platforms via using torch.tensordot and tf.tensordot' but does not specify their version numbers or other software dependencies with versions. |
| Experiment Setup | Yes | On CIFAR-10/100 L is selected with = 8/255, and PGD with step size α = 2/255 serves to generate adversarial examples. Here the number of PGD iterations for training and testing are 10 and 50, respectively. Other hyperparameter settings can be found in the Appendix. |