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..
Understanding and Improving Ensemble Adversarial Defense
Authors: Yian Deng, Tingting Mu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Being tested over various existing ensemble adversarial defense techniques, i GAT is capable of boosting their performance by up to 17% evaluated using CIFAR10 and CIFAR100 datasets under both white-box and black-box attacks. |
| Researcher Affiliation | Academia | Yian Deng Department of Computer Science The University of Manchester Manchester, UK, M13 9PL EMAIL Tingting Mu Department of Computer Science The University of Manchester Manchester, UK, M13 9PL EMAIL |
| Pseudocode | No | The paper describes the proposed methods and their steps in narrative text, but it does not include a formally structured 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The source codes and pre-trained models can be found at https://github.com/xqsi/i GAT. |
| Open Datasets | Yes | CIFAR-10 and CIFAR-100 datasets are used for evaluation, both containing 50,000 training and 10,000 test images [51]. |
| Dataset Splits | No | The paper mentions '50,000 training and 10,000 test images' for CIFAR-10 and CIFAR-100, but does not explicitly specify a separate validation split size or percentage. |
| Hardware Specification | Yes | Each experimental run used one NVIDIA V100 GPU plus 8 CPU cores. |
| Software Dependencies | No | The paper mentions various attacks (PGD, CW, SH, AA) and models (Res Net-20), but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | The two hyper-parameters are set as α = 0.25 and β = 0.5 for So E, while α = 5 and β = 10 for ADP, CLDL and DVERGE, found by grid search. The i GAT training uses a batch size of 512, and multi-step leaning rates of {0.01, 0.002} for CIFAR10 and {0.1, 0.02, 0.004} for CIFAR100. |