On Characterizing GAN Convergence Through Proximal Duality Gap
Authors: Sahil Sidheekh, Aroof Aimen, Narayanan C Krishnan
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we validate experimentally the usefulness of proximal duality gap for monitoring and influencing GAN training.5. Experimentation To experimentally establish the proficiency of DGλ, we consider a WGAN with weight-clipping (that optimizes Vw) (Arjovsky et al., 2017) and a Spectral Normalized GAN (SNGAN) (that optimizes Vc) (Miyato et al., 2018) over 3 datasets MNIST (Deng, 2012), CIFAR-10 (Krizhevsky et al., 2014) and CELEB-A (Liu et al., 2015). |
| Researcher Affiliation | Academia | 1Department of Computer Science, Indian Institute of Technology, Ropar, India. Correspondence to: Sahil Sidheekh <2017csb1104@iitrpr.ac.in>, Narayanan C Krishnan <ckn@iitrpr.ac.in>. |
| Pseudocode | No | The algorithm for the overall estimation process and the associated computational complexity are discussed in the supplementary material. |
| Open Source Code | Yes | Further implementation details for each experiment are provided in the supplementary material and the source code is publicly available 1. 1https://github.com/proximal-dg/proximal_ dg |
| Open Datasets | Yes | We consider a WGAN with weight-clipping... and a Spectral Normalized GAN (SNGAN)... over 3 datasets MNIST (Deng, 2012), CIFAR-10 (Krizhevsky et al., 2014) and CELEB-A (Liu et al., 2015). |
| Dataset Splits | No | The paper mentions training on specific datasets (MNIST, CIFAR-10, CELEB-A) but does not provide explicit training, validation, and test splits (e.g., percentages or sample counts) or references to predefined splits. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | We used the torchgan framework (Pal & Das, 2019) to train and evaluate all GAN models. |
| Experiment Setup | Yes | For all the experiments, we use the 4-layer DCGAN (Radford et al., 2016) architecture for both the generator and the discriminator networks, and an Adam optimizer (Kingma & Ba, 2015) to train the models. To compute DGλ, we use λ=0.1 and 20 optimization steps for approximating the proximal objective.We train WGAN over the MNIST dataset by performing a grid search over N in the range 10 to 10 |