PhD Seminar • Artificial Intelligence • Few Shot Image Generation Using Conditional Set-Based GANs

Please note: This PhD seminar will take place online.

Kira Selby, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Pascal Poupart

While there have been tremendous advances made in few-shot and zero-shot image generation in recent years, one area that remains comparatively underexplored is few-shot generation of images conditioned on sets of unseen images. Existing methods typically condition on a single image only and require strong assumptions about the similarity of the latent distribution of unseen classes relative to training classes.

In contrast, this work proposes SetGAN — a conditional, set-based GAN that learns to generate sets of images conditioned on reference sets from unseen classes. SetGAN can combine information from multiple reference images, as well as generate diverse sets of images which mimic the factors of variation within the reference class.

To attend this PhD seminar on Zoom, please go to https://vectorinstitute.zoom.us/j/86940853104.