Robustness of conditional GANs to noisy labels
Authors: Kiran K. Thekumparampil, Ashish Khetan, Zinan Lin, Sewoong Oh
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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. |