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..
Wasserstein Generative Adversarial Networks
Authors: Martin Arjovsky, Soumith Chintala, Léon Bottou
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, we empirically show that WGANs cure the main training problems of GANs. |
| Researcher Affiliation | Collaboration | 1Courant Institute of Mathematical Sciences, NY 2Facebook AI Research, NY. |
| Pseudocode | Yes | Algorithm 1 WGAN, our proposed algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | We run experiments on image generation. The target distribution to learn is the LSUN-Bedrooms dataset (Yu et al., 2015) a collection of natural images of indoor bedrooms. |
| Dataset Splits | No | The paper mentions using a critic for evaluation and plotting learning curves, but does not provide specific details on validation dataset splits (e.g., percentages, sample counts, or methodologies for creating a validation set). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions optimizers like RMSProp and Adam, but does not provide specific version numbers for any software libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | All experiments in the paper used the default values α = 0.00005, c = 0.01, m = 64, ncritic = 5. ... We use the hyper-parameters specified in Algorithm 1 for all of our experiments. |