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..
Improving Generalization and Stability of Generative Adversarial Networks
Authors: Hoang Thanh-Tung, Truyen Tran, Svetha Venkatesh
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and large scale datasets verify our theoretical analysis. |
| Researcher Affiliation | Academia | Hoang Thanh-Tung EMAIL Truyen Tran EMAIL Svetha Venkatesh EMAIL |
| Pseudocode | Yes | Algorithm 1: Path ο¬nding algorithm |
| Open Source Code | Yes | The code is made available at https://github.com/htt210/ Generalization And Stability In GANs. |
| Open Datasets | Yes | MNIST DATASET and When trained on Imange Net (Deng et al., 2009) |
| Dataset Splits | No | No explicit training/test/validation split percentages or counts are provided. The paper generally refers to 'training dataset' and 'held-out dataset' without specific split information. |
| Hardware Specification | No | No specific hardware details (e.g., GPU models, CPU types, memory amounts) are mentioned for running the experiments. |
| Software Dependencies | No | The paper mentions 'Pytorch (Paszke et al., 2017)' and 'Adam optimizer (Kingma & Ba, 2014)' but does not provide specific version numbers for Pytorch or other ancillary software components needed for reproduction. |
| Experiment Setup | Yes | Hyper parameters for synthetic and MNIST experiments: Learning rate 0.003 for both G and D Learning rate TTUR 0.003 for G, 0.009 for D and the full configuration for Imagenet: generator: name: resnet2 kwargs: nfilter: 32 nfilter_max: 512 embed_size: 128 discriminator: name: resnet2 kwargs: nfilter: 32 nfilter_max: 512 embed_size: 128 z_dist: type: gauss dim: 128 training: out_dir: ../output/imagenet_wgangp5_TTUR gan_type: wgan reg_type: wgangp reg_param: 10. batch_size: 64 nworkers: 32 take_model_average: true model_average_beta: 0.999 model_average_reinit: false monitoring: tensorboard sample_every: 1000 sample_nlabels: 20 inception_every: 10000 save_every: 900 backup_every: 100000 restart_every: -1 optimizer: adam lr_g: 0.0001 lr_d: 0.0003 lr_anneal: 1. lr_anneal_every: 150000 d_steps: 5 equalize_lr: false |