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..
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Authors: Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate that ATMC achieves remarkably favorable trade-offs between robustness and model compactness, we carefully design experiments on a variety of popular datasets and models as summarized in Section 3.1. |
| Researcher Affiliation | Collaboration | Department of Computer Science, University of Rochester Department of Computer Science and Engineering, Texas A&M University Ytech Seattle AI lab, Fe DA lab, AI platform, Kwai Inc |
| Pseudocode | Yes | Algorithm 1 Zero Kmeans B( U) and Algorithm 2 ATMC |
| Open Source Code | Yes | The codes are publicly available at: https://github.com/shupenggui/ATMC. |
| Open Datasets | Yes | Le Net on the MNIST dataset [59]; Res Net34 [60] on CIFAR-10 [61] and CIFAR-100 [61]; and Wide Res Net [62] on SVHN [63]. |
| Dataset Splits | No | The paper mentions training and testing sets, but does not explicitly provide details about a distinct validation dataset split or its size/percentage. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments, such as GPU models, CPU types, or memory details. |
| Software Dependencies | No | The paper mentions using PyTorch implicitly through common deep learning practices but does not specify exact versions for software dependencies like PyTorch, Python, or CUDA. |
| Experiment Setup | Yes | Unless otherwise specified, we set the perturbation magnitude to be 76 for MNIST and 4 for the other three datasets. (The color scale of each channel is between 0 and 255.) Following the settings in [32], we set PGD attack iteration numbers n to be 16 for MNIST and 7 for the other three datasets. We follow [30] to set PGD attack step size α to be min( + 4, 1.25 )/n. We train ATMC for 50, 150, 150, 80 epochs on MNIST, CIFAR10, CIFAR100 and SVHN respectively. |