Adversarial Self-Supervised Contrastive Learning
Authors: Minseon Kim, Jihoon Tack, Sung Ju Hwang
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our method, Robust Contrastive Learning (Ro CL), on multiple benchmark datasets, on which it obtains comparable robust accuracy over state-of-the-art supervised adversarial learning methods, and significantly improved robustness against the black box and unseen types of attacks. |
| Researcher Affiliation | Collaboration | Minseon Kim1, Jihoon Tack1, Sung Ju Hwang1,2 KAIST1, AITRICS2 {minseonkim, jihoontack, sjhwang82}@kaist.ac.kr |
| Pseudocode | Yes | Algorithm 1 Robust Contrastive Learning (Ro CL) |
| Open Source Code | Yes | The code to reproduce the experimental results is available at https://github.com/Kim-Minseon/Ro CL. |
| Open Datasets | Yes | We validate our method... on multiple benchmark datasets (CIFAR-10 and CIFAR-100) |
| Dataset Splits | No | The paper mentions training on CIFAR-10 and CIFAR-100 datasets but does not explicitly provide specific details about the training/validation/test splits, such as percentages or sample counts, nor does it cite standard splits explicitly within the main text regarding data partitioning for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions) used for the experiments. |
| Experiment Setup | Yes | For all baselines and our method, we train with ℓ attacks with the same attack strength of ϵ = 8/255. All ablation studies are conducted with Res Net18 trained on CIFAR-10, with the attack strength of ϵ = 8/255. Regarding the additional results on CIFAR-100 and details of the optimization & evaluation, please see the Appendix A, and C. |