Generative Warfare Nets: Ensemble via Adversaries and Collaborators
Authors: Honglun Zhang, Liqiang Xiao, Wenqing Chen, Yongkun Wang, Yaohui Jin
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 |