self-paced learning

**Self-Paced Learning** is a **curriculum learning variant where the model itself decides which training examples to include** — the model's own loss on each example determines difficulty, and a pace parameter controls how many "hard" examples are included as training progresses. **Self-Paced Formulation** - **Loss Threshold**: Include example $i$ if $L(x_i) < lambda$ — low-loss examples are "easy" and included first. - **Pace Parameter ($lambda$)**: Increases over training — starts with only easy examples, gradually includes harder ones. - **Binary Variable**: $v_i in {0,1}$ indicates whether example $i$ is included in the current training set. - **Joint Optimization**: Alternate between optimizing model parameters $ heta$ and sample weights $v$. **Why It Matters** - **No External Teacher**: Unlike standard curriculum learning, self-paced learning doesn't need a difficulty oracle — the model defines its own curriculum. - **Robust to Noise**: Noisy/mislabeled examples have high loss — they are naturally excluded until late in training. - **Autonomous**: The model autonomously manages its own learning pace. **Self-Paced Learning** is **the model teaches itself** — automatically selecting training examples by difficulty based on its own evolving understanding.