Code and Supplementary Materials for: Comparison of Conventional and Generative Methods in Synthetic Data Generation
Repository supporting our paper "Conventional Augmentation is More Effective than ImageGPT and GANs? A Comparison of Synthetic Data Evaluation Methods"
Key files:
reproduce_results.ipynb: Notebook for reproducing all of the resultsimg_transformation.py: Our conventional augmentation function, comprising of transformations fromtorchvision