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 Well-intentioned Networks
Authors: Justin Cosentino, Jun Zhu
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then empirically evaluate the effectiveness of the proposed framework in Section 5. Lastly, we discuss related works in Section 6.We evaluate the WGWIN-GP using the training procedure outlined in Section 4 and the inference method illustrated in Figure 1. |
| Researcher Affiliation | Academia | Justin Cosentino, Jun Zhu Dept. of Comp. Sci. & Tech., Institute for AI, THBI Lab, BNRist Center, State Key Lab for Intell. Tech. & Sys., Tsinghua University, Beijing, China |
| Pseudocode | Yes | Algorithm 1: WGWIN with gradient and transformation penalty. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We use two different datasets in our experiments: the MNIST handwritten digits [23] dataset and the Fashion-MNIST clothing dataset [41]. |
| Dataset Splits | Yes | We further split both training sets into a 50,000 image training set and 10,000 image validation set. |
| Hardware Specification | Yes | We trained and evaluated the models using NVIDIA Ge Force GTX TITAN X GPUs. |
| Software Dependencies | No | The network is implemented using Tensor Flow Probability [7]. No specific version numbers for software dependencies are provided. |
| Experiment Setup | Yes | The BNN trained for 30 epochs using a learning rate of 0.001 and batch size of 128. The GWIN trained for 200,000 iterations using the default hyperparameters listed in Algorithm 1. Both the generator and critic used a learning rate of 0.0001 and batch size of 128. |