coarse-to-fine training

**Coarse-to-Fine Training** is a **hierarchical training strategy that first learns coarse, global patterns, then progressively refines to learn fine-grained, local details** — structuring the learning process from the big picture to the details. **Coarse-to-Fine Approaches** - **Resolution**: Start with low-resolution inputs (coarse spatial features), increase resolution for fine details. - **Label Hierarchy**: First learn coarse categories (defect vs. no-defect), then fine categories (defect type). - **Loss Weighting**: Start with losses that emphasize global structure, shift to losses for local detail. - **Architecture**: Train shallow layers first (coarse features), then progressively train deeper layers (fine features). **Why It Matters** - **Curriculum**: Provides a natural curriculum — easy coarse task first, hard fine-grained task later. - **Stability**: Coarse features provide a stable foundation for learning fine details. - **Semiconductor**: Defect classification naturally follows coarse-to-fine — classified by severity first, type, then root cause. **Coarse-to-Fine Training** is **learning the outline before the details** — structuring training to build from global understanding to fine-grained precision.