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..
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle
Authors: Dinghuai Zhang, Tianyuan Zhang, Yiping Lu, Zhanxing Zhu, Bin Dong
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate that YOPO can achieve comparable defense accuracy with approximately 1/5 1/4 GPU time of the projected gradient descent (PGD) algorithm. |
| Researcher Affiliation | Academia | Dinghuai Zhang , Tianyuan Zhang Peking University EMAIL; Yiping Lu Stanford University EMAIL; Zhanxing Zhu School of Mathematical Sciences, Peking University Center for Data Science, Peking University Beijing Institute of Big Data Research EMAIL; Bin Dong Beijing International Center for Mathematical Research, Peking University Center for Data Science, Peking University Beijing Institute of Big Data Research EMAIL |
| Pseudocode | Yes | Algorithm 1 YOPO (You Only Propagate Once) |
| Open Source Code | Yes | Our codes are available at https://github.com/a1600012888/YOPO-You-Only-Propagate-Once |
| Open Datasets | Yes | To demonstrate the effectiveness of YOPO, we conduct experiments on MNIST and CIFAR10. |
| Dataset Splits | No | The paper mentions using MNIST and CIFAR10 but does not specify the training, validation, and test dataset splits (e.g., percentages or sample counts). |
| Hardware Specification | No | The paper mentions "GPU time" but does not specify the type or model of GPUs, CPUs, or any other hardware components used for experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with their version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | As a comparison, we test YOPO-3-5 and YOPO-5-3 with a step size of 2/255. |