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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Spear: Evaluate the Adversarial Robustness of Compressed Neural Models
Authors: Chong Yu, Tao Chen, Zhongxue Gan, Jiayuan Fan
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the proposed Spear attack technique can generally be applied to various networks and tasks through quantitative and ablation experiments. |
| Researcher Affiliation | Academia | Chong Yu1 , Tao Chen2, , Zhongxue Gan1, and Jiayuan Fan1 1Academy for Engineering and Technology, Fudan University 2School for Information Science and Technology, Fudan University |
| Pseudocode | No | The paper includes mathematical formulations and workflow diagrams but no structured pseudocode or algorithm blocks labeled as such. |
| Open Source Code | No | The paper links to third-party tools and models (e.g., PyTorch, NVIDIA libraries, pre-trained models) that were used, but it does not provide a link or explicit statement about the availability of the source code for the Spear attack methodology developed in this paper. |
| Open Datasets | Yes | large-scale datasets, like Image Net [Deng et al., 2009], COCO [Lin et al., 2014], etc. ... MNIST [Le Cun et al., 1998], CIFAR-10 [Krizhevsky et al., 2009], and CIFAR-100 [Krizhevsky et al., 2009]. |
| Dataset Splits | No | The paper mentions using a 'test set' for evaluation and 'training dataset' in the ablation study but does not explicitly provide the specific train/validation/test dataset splits (e.g., percentages or sample counts) for their experiments. |
| Hardware Specification | Yes | All of the state-of-the-art methods and Spear attack training and fine-tuning experimental results are obtained with V100 [NVIDIA, 2017] and A100 [NVIDIA, 2020a] GPU clusters. |
| Software Dependencies | Yes | we choose Py Torch [Paszke et al., 2017] with version 1.8.0 as the framework to implement all algorithms. |
| Experiment Setup | Yes | The loss adjustment parameters among the prediction loss (α1, α2), the distillation loss (β1, β2) and the adversarial loss (γ) apply 1, 1.5, 2, 4, 1, respectively. ... apply 1, 1.5, 3, 6, 1.5, respectively. |