Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Authors: Bingchen Liu, Yizhe Zhu, Kunpeng Song, Ahmed Elgammal
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We propose a light-weight GAN structure that gains superior quality on 1024 1024 resolution. Notably, the model converges from scratch with just a few hours of training on a single RTX-2080 GPU, and has a consistent performance, even with less than 100 training samples. |
| Researcher Affiliation | Collaboration | 1Playform Artrendex Inc., USA 2Department of Computer Science, Rutgers University |
| Pseudocode | No | The paper illustrates model structures with figures (Fig. 3 and Fig. 4) but does not contain pseudocode or algorithm blocks. |
| Open Source Code | Yes | The datasets and code are available at: https://github.com/odegeasslbc/Fast GAN-pytorch |
| Open Datasets | Yes | The datasets and code are available at: https://github.com/odegeasslbc/Fast GAN-pytorch and On 256 256 resolution, we test on Animal-Face Dog and Cat (Si & Zhu, 2011), 100-Shot-Obama, Panda, and Grumpy-cat (Zhao et al., 2020). On 1024 1024 resolution, we test on Flickr-Face HQ (FFHQ) (Karras et al., 2019), Oxford-flowers (Nilsback & Zisserman, 2006), art paintings from Wiki Art (wikiart.org), photographs on natural landscape from Unsplash (unsplash.com), Pokemon (pokemon.com), anime face, skull, and shell. |
| Dataset Splits | No | The paper mentions a training/testing ratio of 9:1 for latent space back-tracking, but does not explicitly state specific train/validation/test splits with percentages or counts for the main GAN training. |
| Hardware Specification | Yes | single RTX-2080 GPU, single RTX 2080-Ti GPU, Nvidia s RTX 2080-Ti GPU, RTX TITAN GPU. |
| Software Dependencies | No | The paper states 'implemented using Py Torch (Paszke et al., 2017)' but does not provide a specific version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We train the models 5 times with random seeds, We employ the hinge version of the adversarial loss, batch-size of 8, batch-size of 16, batch-size of 32. |