The relativistic discriminator: a key element missing from standard GAN
Authors: Alexia Jolicoeur-Martineau
ICLR 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we observe that 1) RGANs and Ra GANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts, 2) Standard Ra GAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and 3) Ra GANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization. The code is freely available on https://github.com/Alexia JM/Relativistic GAN. |
| Researcher Affiliation | Academia | Alexia Jolicoeur-Martineau Lady Davis Institute MILA, Université de Montréal Montreal, Canada alexia.jolicoeur-martineau@mail.mcgill.ca |
| Pseudocode | Yes | H ALGORITHMS Algorithm 1 Training algorithm for non-saturating RGANs with symmetric loss functions |
| Open Source Code | Yes | The code is freely available on https://github.com/Alexia JM/Relativistic GAN. |
| Open Datasets | Yes | Experiments were conducted on the CIFAR-10 dataset (Krizhevsky, 2009) and the CAT dataset (Zhang et al., 2008). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits needed to reproduce the experiment. It mentions using CIFAR-10 and CAT datasets but does not detail how they were split for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide SPECIFIC HARDWARE DETAILS (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | Code was written in Pytorch (Paszke et al., 2017) and models were trained using the Adam optimizer (Kingma & Ba, 2014). The paper mentions PyTorch but does not specify its version number or any other software dependencies with specific versions. |
| Experiment Setup | Yes | We used the following two known stable setups: (DCGAN setup) lr = .0002, n D = 1, β1 = .50 and β2 = .999 (Radford et al., 2015), and (WGAN-GP setup) lr = .0001, n D = 5, β1 = .50 and β2 = .9 (Gulrajani et al., 2017), where lr is the learning rate, n D is the number of discriminator updates per generator update, and β1, β2 are the ADAM momentum parameters. For optimal stability, we used batch norm (Ioffe & Szegedy, 2015) in G and spectral norm (Miyato et al., 2018) in D. |