Mask Generation
term_id: mask_generation
Category: application_paradigms
Definition
Mask generation involves producing spatial or temporal masks that determine which elements of a dataset are visible or active during specific operations. In computer vision, it is used for object segmentation or inpainting, where masks define regions of interest. In natural language processing, causal masks prevent attention mechanisms from accessing future tokens. This technique allows models to focus on relevant features, handle missing data, or enforce structural constraints during inference and training.
Summary
The process of creating binary or probabilistic masks to selectively hide or emphasize parts of input data during model processing.
Key Concepts
- Binary masking
- Attention masks
- Inpainting
- Feature selection
Use Cases
- Image inpainting and restoration
- Transformer attention mechanisms
- Object detection and segmentation