<p>A couple of ways to do it is by:</p>
<ol>
<li>freezing the lower layers of the discriminator,</li>
<li>changing the embeddings.</li>
</ol>
<p>References:</p>
<ol>
<li>Mo S, Cho M, Shin J. Freeze the discriminator: a simple baseline for fine-tuning gans. arXiv preprint arXiv:2002.10964. 2020 Feb 25. <a href="https://arxiv.org/pdf/2002.10964.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2002.10964.pdf</a>
Code: <a href="https://github.com/sangwoomo/FreezeD" rel="nofollow noreferrer">https://github.com/sangwoomo/FreezeD</a></li>
<li>Li Q, Mai L, Alcorn MA, Nguyen A. A cost-effective method for improving and re-purposing large, pre-trained GANs by fine-tuning their class-embeddings. InProceedings of the Asian Conference on Computer Vision 2020. <a href="https://anhnguyen.me/project/biggan-am/" rel="nofollow noreferrer">https://anhnguyen.me/project/biggan-am/</a>
Code: <a href="https://github.com/qilimk/biggan-am" rel="nofollow noreferrer">https://github.com/qilimk/biggan-am</a></li>
</ol>
How to fine tune a pre-trained GAN?
<p>I would like to fine tune a pre-trained GAN available online using my own images. For example, BigGAN, which was trained on ImageNet, can generate realistic images. However, I do not want to generate the classes of images in ImageNet. I want to generate artificial images of my own image sets. How can I fine tune the pre-train models? Is it the same as fine-tuning other neural networks like a CNN image classification model? Is just replacing/retrain the last few layers is enough? It would be nice if I have see some examples in code of Tensorflow/Keras. Thanks so much!</p>
<p>BigGAN
<a href="https://tfhub.dev/deepmind/biggan-deep-256/1" rel="nofollow noreferrer">https://tfhub.dev/deepmind/biggan-deep-256/1</a></p>