curriculum learning for vision

**Curriculum Learning for Vision** is the **training of visual models by presenting training samples in a meaningful order** — starting with easy, clear examples and gradually introducing harder, more ambiguous ones, mimicking how humans learn visual recognition. **Curriculum Strategies for Vision** - **Difficulty Scoring**: Rank images by difficulty (loss, confidence, diversity) — a teacher model or heuristic defines difficulty. - **Pacing Function**: Linear, exponential, or step pacing determines how fast hard examples are introduced. - **Self-Paced**: The model itself determines which samples it's ready to learn — based on its own loss. - **Anti-Curriculum**: Some works show starting with hard examples can be beneficial (contradicts the standard curriculum). **Why It Matters** - **Faster Convergence**: Curriculum learning can speed up convergence by avoiding "confusion" from hard examples early on. - **Better Generalization**: Structured exposure to easy → hard produces more robust learned features. - **Noisy Labels**: Curriculum learning naturally deprioritizes noisy/mislabeled examples (which appear "hard"). **Curriculum Learning** is **teach the easy stuff first** — ordering training samples by difficulty for smoother, faster, and better visual model training.