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..
Training GANs with Stronger Augmentations via Contrastive Discriminator
Authors: Jongheon Jeong, Jinwoo Shin
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results show that GANs with Contra D consistently improve FID and IS compared to other recent techniques incorporating data augmentations |
| Researcher Affiliation | Academia | 1School of Electrical Engineering 2Graduate School of AI Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141, South Korea EMAIL |
| Pseudocode | Yes | Algorithm 1 in Appendix A describes a concrete training procedure of GANs with Contra D using Adam optimizer (Kingma & Ba, 2014). |
| Open Source Code | Yes | Code is available at https://github.com/jh-jeong/Contra D. |
| Open Datasets | Yes | We consider a variety of datasets including CIFAR-10/100 (Krizhevsky, 2009), Celeb A-HQ-128 (Lee et al., 2020), AFHQ (Choi et al., 2020) and Image Net (Russakovsky et al., 2015) in our experiments |
| Dataset Splits | Yes | CIFAR-10 and CIFAR-100 (Krizhevsky, 2009) consist of 60K images of size 32 32 in 10 and 100 classes, respectively, 50K for training and 10K for testing. |
| Hardware Specification | No | The paper does not mention any specific CPU or GPU models, or detailed specifications of the hardware used for experiments. |
| Software Dependencies | No | All the models are implemented in Py Torch (Paszke et al., 2019) framework. |
| Experiment Setup | Yes | We provide the detailed specification on the experimental setups, e.g., architectures, training configurations and hyperparameters in Appendix F. |