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].
TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness
Authors: Zhuolin Yang, Linyi Li, Xiaojun Xu, Shiliang Zuo, Qian Chen, Pan Zhou, Benjamin Rubinstein, Ce Zhang, Bo Li
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on TRS and compare with 6 state-of-the-art ensemble baselines against 8 whitebox attacks on different datasets, demonstrating that the proposed TRS outperforms all baselines significantly. |
| Researcher Affiliation | Collaboration | 1 University of Illinois Urbana-Champaign 2 Tencent Inc. 3 University of Melbourne 4 Huazhong University of Science and Technology 5 ETH Zurich |
| Pseudocode | Yes | We present one-epoch training pseudo code in Algorithm 1 of Appendix F. |
| Open Source Code | Yes | The code is publicly available2. 2https://github.com/AI-secure/Transferability-Reduced-Smooth-Ensemble |
| Open Datasets | Yes | We conduct our experiments on widely-used image datasets including hand-written dataset MNIST [29]; and colourful image datasets CIFAR-10 and CIFAR-100 [26]. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about validation splits (e.g., percentages or sample counts) in the main text. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware (e.g., GPU model, CPU, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., PyTorch 1.9, TensorFlow 2.x). |
| Experiment Setup | Yes | The detailed hyper-parameter setting and training criterion are discussed in Appendix F. |