Synthetic unknown class learning for learning unknowns

Jaeyeon Jang

Abstract: Highlights•A novel generative open set recognition (OSR) model is developed.•The limitation of generative OSR models that generate limited samples is addressed.•A new learning technique generates realistic unknown-like samples and learns them.•Knowledge distillation is employed to reduce overgeneralization on known classes.