TTUR facilitates stable training for GANs by employing a two-stage update rule. This approach decouples the generator and discriminator learning rates, allowing for more controlled optimization. The core problem addressed is the instability often encountered when training GANs with standard update strategies, leading to oscillations and mode collapse. This repository provides implementations and resources for using TTUR in GAN training, primarily utilizing TensorFlow.
This project provides a practical implementation of the TTUR method, simplifying its integration into existing GAN training pipelines. It includes precalculated statistics for FID evaluation, offering a convenient way to assess generator performance. The repository also addresses a critical bug in previous versions of the FID calculation, ensuring reliable results. Includes a WGAN-GP implementation.
- FID Calculation: Provides a robust implementation for calculating the Fréchet Inception Distance, a key metric for GAN evaluation.
- WGAN-GP Implementation: Offers an improved WGAN-GP implementation with TTUR and Tensorboard logging.
- Precalculated Statistics: Supplies precalculated FID statistics for various datasets (CelebA, LSUN, CIFAR10, SVHN, ImageNet) to streamline evaluation.
- Flexible Usage: Can be used as a Python module or as a standalone script for FID calculations.
- Example Scripts: Includes example scripts for FID calculation and precalculated statistics generation.
- Compatibility: Supports TensorFlow 1.1+ and Python 3.x.
- Clear Documentation: Provides instructions for using the code and accessing precalculated statistics.
The project is actively maintained, with recent commits and ongoing developments. The documentation includes clear instructions and usage examples. The availability of precalculated statistics and the included example scripts enhance usability. While dependencies are being actively updated to support current environments the code demonstrates a solid and well-tested foundation for GAN research.
This project benefits researchers and practitioners working with GANs by offering a reliable and efficient implementation of the TTUR training method. It enables more stable GAN training, facilitates evaluation using FID and provides readily available statistics for common datasets. The project simplifies the process of applying TTUR, offering value over manual tuning of learning rates and providing a tool for assessing generative model quality.