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 [1].
Input-Specific Robustness Certification for Randomized Smoothing
Authors: Ruoxin Chen, Jie Li, Junchi Yan, Ping Li, Bin Sheng6295-6303
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The empirical results on CIFAR-10 and Image Net show that ISS can speed up the certification by more than three times at a limited cost of 0.05 certified radius. Meanwhile, ISS surpasses IAS on the average certified radius across the extensive hyperparameter settings. |
| Researcher Affiliation | Academia | 1 Shanghai Jiao Tong University 2 The Hong Kong Polytechnic University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Compute ISS mapping ψISS( ) |
| Open Source Code | Yes | We release our code in https: //github.com/roy-ch/Input-Specific-Certification. |
| Open Datasets | Yes | We evaluate our proposed method ISS on two benchmark datasets: CIFAR-10 (Krizhevsky 2009) and Image Net (Russakovsky et al. 2015). |
| Dataset Splits | No | The paper mentions using test data but does not explicitly provide specific training, validation, and test dataset splits with percentages or sample counts for reproduction. |
| Hardware Specification | Yes | All the experiments are conducted on CPU (16 Intel(R) Xeon(R) Gold 5222 CPU @ 3.80GHz) and GPU (one NVIDIA RTX 2080 Ti). |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The hyperparameters are listed in Table 2. |