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..
Towards Robust Detection of Adversarial Examples
Authors: Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply our method to defend various attacking methods on the widely used MNIST and CIFAR-10 datasets, and achieve significant improvements on robust predictions under all the threat models in the adversarial setting. |
| Researcher Affiliation | Academia | Tianyu Pang, Chao Du, Yinpeng Dong, Jun Zhu Dept. of Comp. Sci. & Tech., State Key Lab for Intell. Tech. & Systems BNRist Center, THBI Lab, Tsinghua University, Beijing, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods in text and uses mathematical formulas, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a concrete link to source code or explicitly state that source code is available for the methodology described. |
| Open Datasets | Yes | We use the two widely studied datasets MNIST [20] and CIFAR-10 [17]. MNIST is a collection of handwritten digits with a training set of 60,000 images and a test set of 10,000 images. CIFAR-10 consists of 60,000 color images in 10 classes with 6,000 images per class. There are 50,000 training images and 10,000 test images. |
| Dataset Splits | No | The paper specifies training and test set sizes for MNIST and CIFAR-10, but it does not explicitly mention a separate validation set split or how data was partitioned for validation. |
| Hardware Specification | No | The paper mentions funding from "NVIDIA NVAIL Program, and the projects from Siemens and Intel" in the acknowledgements, but it does not specify the exact hardware (e.g., specific GPU/CPU models, memory details) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names like PyTorch or TensorFlow with their respective versions) needed to replicate the experiments. |
| Experiment Setup | Yes | For each network, we use both the CE and RCE as the training objectives, trained by the same settings as He et al. [16]. The number of training steps for both objectives is set to be 20,000 on MNIST and 90,000 on CIFAR-10. The pixel values of images in both datasets are scaled to be in the interval [ 0.5, 0.5]. |