Using pre-trained GANs
One of the challenges of GANs is how resource-intensive their training is. Coupled with the problems of training instability and model collapse, this highlights the advantages of using pre-trained models. Doing so harnesses the investment of companies such as NVIDIA, who invest heavily in state-of-the-art and groundbreaking GANs such as the StyleGAN series.
You can use them out of the box or fine-tune them for your custom domain needs. Here is a list of some of the most popular pre-trained GANs.
Pix2Pix
Let’s learn more about Pix2Pix:
- Developed by: Pix2Pix was developed by Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros at UC Berkeley in 2016 and updated in 2018.
- How it works: Pix2Pix is a conditional GAN for paired image-to-image translation. A conditional GAN is a type of GAN that generates images conditioned on some additional information, such as class labels, text descriptions, or even other images, enabling the...
