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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting
Authors: Chengyu Dong, Liyuan Liu, Jingbo Shang
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on different datasets, training methods, neural architectures and robustness evaluation metrics verify the effectiveness of our method. |
| Researcher Affiliation | Collaboration | Chengyu Dong University of California, San Diego EMAIL Liyuan Liu Microsoft Research EMAIL Jingbo Shang University of California, San Diego EMAIL |
| Pseudocode | No | The paper describes its methods in text and mathematical formulations but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We conduct experiments on three datasets including CIFAR-10, CIFAR100 (Krizhevsky, 2009) and Tiny-Image Net (Le & Yang, 2015). |
| Dataset Splits | No | The paper mentions using a 'validation set' and 'training subset of size 5k' but does not provide specific split percentages or sample counts for the train/validation/test sets across all experiments to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various training methods and models (e.g., PGD training, Res Net-18, Auto Attack) but does not provide specific version numbers for any software dependencies or libraries used. |
| Experiment Setup | Yes | We conduct PGD training on pre-activation Res Net-18 (He et al., 2016) with 10 iterations and perturbation radius 8/255 by default. |