Winter Term 2020/21 Machine Learning @ Free University Berlin

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Published Jan 31, 2021

There is so much more we wanted to include in this video, but unfortunately we had to trim down the content to avoid overwhelming you.

However, for those of you who are keen and interested enough to dig deeper, we want to provide some additional material. :)

Here are some read-worthy and landmark papers on GANs which we could only cover briefly or had to remove entirely:

- Progressive Growing of GANs for Improved Quality, Stability and Variation
https://arxiv.org/pdf/1710.10196.pdf
- Face Aging with Conditional General Adversarial Networks
https://arxiv.org/pdf/1702.01983.pdf
- A Style-Based Generator Architecture for Generative Adversarial Networks
https://arxiv.org/pdf/1812.04948.pdf
- Improved Training of Wasserstein GANs
https://arxiv.org/pdf/1704.00028.pdf
- AVAE: Adversarial Variational Autoencoder
https://arxiv.org/pdf/2012.11551.pdf

If you are interested in the intersection of AI and art, we suggest checking out this paper on Creative Adversarial Networks (CANs):
https://arxiv.org/pdf/1706.07068.pdf

And if you want to unleash the inner artist in you, you can have a go with this live demo of GauGAN from NVIDIA:
http://nvidia-research-mingyuliu.com/gaugan