Training GANs with Stronger Augmentations via Contrastive Discriminator
Authors: Jongheon Jeong, Jinwoo Shin
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | 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 {jongheonj,jinwoos}@kaist.ac.kr |
| 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. |