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.