Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
CSTAR: Towards Compact and Structured Deep Neural Networks with Adversarial Robustness
Authors: Huy Phan, Miao Yin, Yang Sui, Bo Yuan, Saman Zonouz
AAAI 2023 | Venue PDF | 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. |