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The lecture will cover the CVPR 2020 paper - Breaking the cycle - Colleagues are all you need. Research is presenting a new GAN method to do image style transfer.

Presenter BIO:

Paper's author Ori Nizan is a PhD student at the Department of Electrical Engineering, Technion, CGM Lab, suprevised by Professor Ayellet Tal

His main field of study is Image domain transfer.

Website: https://onr.github.io/

Lecture abstract:

This paper proposes a novel approach to performing image-to-image translation between unpaired domains. Rather than relying on a cycle constraint, our method takes advantage of collaboration between various GANs. This results in a multi modal method, in which multiple optional and diverse images are produced for a given image. Our model addresses some of the shortcomings of classical GANs: (1) It is able to remove large objects, such as glasses. (2) Since it does not need to support the cycle constraint, no irrelevant traces of the input are left on the generated image. (3) It manages to translate between domains that require large shape modifications. Our results are shown to outperform those generated by state-of-the-art methods for several challenging applications on commonly-used datasets, both qualitatively and quantitatively.

Paper's git: https://github.com/Onr/Council-GAN

This is a technical talk, prior knowledge of GANs is advised.

** ** Please register through the zoom link right after your RSVP. We will send the links to the zoom event via email only to those who have registered through zoom. ** **

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