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..
Improving Adversarial Robustness Through the Contrastive-Guided Diffusion Process
Authors: Yidong Ouyang, Liyan Xie, Guang Cheng
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical results using simulations and demonstrate the good performance of Contrastive-DP on image datasets. |
| Researcher Affiliation | Academia | 1School of Data Science, The Chinese University of Hong Kong, Shenzhen, China 2Department of Statistics, University of California, Los Angeles, USA. |
| Pseudocode | Yes | Algorithm 1 Generation in Contrastive-guided Diffusion Process (Contrastive-DP) |
| Open Source Code | No | The paper does not explicitly provide a link to open-source code or state that the code will be made publicly available for the described methodology. |
| Open Datasets | Yes | We test the contrastive-DP algorithm on three image datasets, the MNIST dataset (Le Cun et al., 1998), CIFAR10 dataset (Krizhevsky, 2009), and the Traffic Signs dataset (Houben et al., 2013). |
| Dataset Splits | No | The paper specifies training and testing sizes for datasets like MNIST ("60k...for training and 10k...for testing"), but it does not explicitly mention validation dataset splits. |
| Hardware Specification | Yes | Running on a RTX 4x2080Ti GPU cluster. |
| Software Dependencies | No | The paper mentions tools and frameworks (e.g., PyTorch, TRADES, WRN-28-10) but does not provide specific version numbers for any software components. |
| Experiment Setup | Yes | A detailed description of the pipeline for generating data and the corresponding hyperparameter can be found in Appendix D.3. |