Presentation
Self-conditioned Generative Adversarial Networks for Image Editing
SessionTechnical Papers
Event Type
Technical Paper
Research & Education
Virtual
Full Conference Supporter
Full Conference
Virtual Conference Supporter
Virtual Conference
Exhibitor Additional Full Conference
Exhibitor Full Conference
Time
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DescriptionGANs are susceptible to generative bias. They focus on the core of the distribution, leaving the edges behind. We argue that this bias is responsible for the collapse of latent-editing methods away from the distribution's core and propose a method for self-conditioning the GAN — improving treatment of rare modalities.
