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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?
Authors: Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Chuang Gan
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 19,775 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,394 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With a thorough experimental study, we demonstrate that ADVCL outperforms the state-of-the-art self-supervised robust learning methods across multiple datasets (CIFAR-10, CIFAR-100 and STL-10) and finetuning schemes (linear evaluation and full model finetuning). |
| Researcher Affiliation | Collaboration | Lijie Fan1, Sijia Liu2, Pin-Yu Chen3, Gaoyuan Zhang3, Chuang Gan3 1 Massachusetts Institute of Technology, 2 Michigan State University, 3 MIT-IBM Watson AI Lab, IBM Research |
| Pseudocode | No | The paper provides a pipeline diagram (Figure 2) but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/Lijie Fan/Adv CL. |
| Open Datasets | Yes | With a thorough experimental study, we demonstrate that ADVCL outperforms the state-of-the-art self-supervised robust learning methods across multiple datasets (CIFAR-10, CIFAR-100 and STL-10) and finetuning schemes (linear evaluation and full model finetuning). |
| Dataset Splits | No | The paper mentions training and testing, and discusses finetuning schemes, but does not explicitly describe a validation dataset split or how it was used. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running its experiments. It only mentions using ResNet-18 as an encoder architecture. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1) needed to replicate the experiment. |
| Experiment Setup | Yes | Unless specified otherwise, we use 5-step 1 projected gradient descent (PGD) with = 8/255 to generate perturbations during pretraining, and use Auto-Attack and 20-step 1 PGD with = 8/255 to generate perturbations in computing AA and RA at test time. |