Template Masks in Deep Learning

• Template Masks are techniques that use learned masks to selectively modulate deep network features, improving recognition accuracy.
• They employ occlusion-guided methodologies to create compact template representations in ensemble models.
• This approach boosts performance in pose-invariant face recognition and other computer vision tasks by improving robustness against occlusions.

Searching arXiv for the specified papers on template masks and related uses. {} {"query":"(Wu et al., 2019) Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition"}