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..
Robustness of conditional GANs to noisy labels
Authors: Kiran K. Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show experimentally on MNIST and CIFAR-10 datasets that both the approaches consistently improve upon baseline approaches, and RCGAN-U closely matches the performance of RCGAN. 5 Experiments |
| Researcher Affiliation | Academia | University of Illinois at Urbana-Champaign, Carnegie Mellon University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to reproduce our experiments is available at https://github.com/POLane16/Robust-Conditional-GAN. |
| Open Datasets | Yes | We show experimentally on MNIST and CIFAR-10 datasets that both the approaches consistently improve upon baseline approaches, and RCGAN-U closely matches the performance of RCGAN. |
| Dataset Splits | No | The paper mentions using training data and pre-trained classifiers for evaluation but does not specify explicit training/validation/test dataset splits for their own model training. |
| Hardware Specification | Yes | This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number OCI-1053575. Specifically, it used the Bridges system, which is supported by NSF award number ACI-1445606, at the Pittsburgh Supercomputing Center (PSC). |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies used in their implementation. |
| Experiment Setup | No | Implementation details are explained in Appendix L. |