Adapt the artistic stylings of NVIDIA Research's GauGAN to your own data.
In this tutorial we'll cover how to adapt GauGAN for custom training, and different techniques that can be used to evaluate its performance.
This notebook has two complementary tutorials on the blog. Both are written by Ayoosh Kathuria:
- Understanding GauGAN Part 2: Training on Custom Datasets
- Understanding GauGAN Part 3: Model Evaluation Techniques
For more information on GauGAN, including details about its architecture, how to debug training, or considerations for implementing it within a company be sure to start with Part 1 and read the entire 4-part series.
GAN, TensorFlow, Educational
By clicking the Run on Gradient button above, you will be launching the contents of this repository into a Jupyter notebook on Paperspace Gradient.
Docs are available at docs.paperspace.com.
Be sure to read about how to create a notebook or watch the video instead!