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..
ViTGAN: Training GANs with Vision Transformers
Authors: Kwonjoon Lee, Huiwen Chang, Lu Jiang, Han Zhang, Zhuowen Tu, Ce Liu
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our approach, named Vi TGAN, achieves comparable performance to the leading CNNbased GAN models on three datasets: CIFAR-10, Celeb A, and LSUN bedroom. |
| Researcher Affiliation | Collaboration | Kwonjoon Lee1,3 Huiwen Chang2 Lu Jiang2 Han Zhang2 Zhuowen Tu1 Ce Liu4 1UC San Diego 2Google Research 3Honda Research Institute 4Microsoft Azure AI EMAIL EMAIL EMAIL EMAIL |
| Pseudocode | No | The paper includes mathematical equations and architectural diagrams (e.g., Figure 1, Figure 2) but no explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Empirically, our approach, named Vi TGAN, achieves comparable performance to the leading CNNbased GAN models on three datasets: CIFAR-10, Celeb A, and LSUN bedroom. Our code is available online1. 1https://github.com/mlpc-ucsd/Vi TGAN |
| Open Datasets | Yes | We train and evaluate our model on various standard benchmarks for image generation, including CIFAR-10 (Krizhevsky et al., 2009), LSUN bedroom (Yu et al., 2015) and Celeb A (Liu et al., 2015). |
| Dataset Splits | Yes | The LSUN bedroom dataset (Yu et al., 2015) is a large-scale image generation benchmark, consisting of 3 million training images and 300 images for validation. On this dataset, FID is computed against the training set due to the small validation set. |
| Hardware Specification | Yes | Both Vi TGAN and Style GAN2 are based on Tensorflow 2 implementation2 and trained on Google Cloud TPU v2-32 and v3-8. |
| Software Dependencies | Yes | Both Vi TGAN and Style GAN2 are based on Tensorflow 2 implementation2 and trained on Google Cloud TPU v2-32 and v3-8. |
| Experiment Setup | Yes | We train our models with Adam with β1 = 0.0, β2 = 0.99, and a learning rate of 0.002 following the practice of (Karras et al., 2020b). In addition, we employ non-saturating logistic loss (Goodfellow et al., 2014), exponential moving average of generator weights (Karras et al., 2018), and equalized learning rate (Karras et al., 2018). We use a mini-batch size of 128. |