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..
Generative Warfare Nets: Ensemble via Adversaries and Collaborators
Authors: Honglun Zhang, Liqiang Xiao, Wenqing Chen, Yongkun Wang, Yaohui Jin
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three natural image datasets show that GWN can achieve state-of-the-art Inception scores and produce diverse high-quality synthetic results. In this section, we conduct detailed discussions about the implementations of GWN, investigate the performances of GWN on three natural image datasets and compare it to stateof-the-art baselines. |
| Researcher Affiliation | Academia | 1State Key Lab of Advanced Optical Communication System and Network, Shanghai Jiao Tong University 2Artificial Intelligence Institute, Shanghai Jiao Tong University 3Network and Information Center, Shanghai Jiao Tong University {jinyh}@sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Training Generative Warfare Nets |
| Open Source Code | No | The paper does not contain any statement about releasing source code or a link to a code repository for the methodology. |
| Open Datasets | Yes | We select three natural image datasets with increasing diversities and sizes to conduct experiments for GWN, CIFAR10 [Krizhevsky and Hinton, 2009], STL-10 [Coates et al., 2011] and Image Net [Russakovsky et al., 2015]. |
| Dataset Splits | No | The paper mentions using CIFAR-10, STL-10, and Image Net datasets but does not explicitly state specific train/validation/test splits (e.g., percentages, sample counts, or citations to standard splits used for partitioning). |
| Hardware Specification | No | The paper does not provide any specific hardware details like CPU/GPU models, memory, or cloud computing specifications used for running experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizers' and 'layer normalization' but does not specify software components with version numbers (e.g., 'Python 3.x', 'PyTorch 1.x') required to replicate the experiment. |
| Experiment Setup | Yes | Description Setting batch size K = 64 gradient penalty λ = 10 discriminator times nd = 5 Adam hyperparameters α = 0.0001, β1 = 0.5, β2 = 0.9 |