multi-exit networks

**Multi-Exit Networks** are **neural networks designed with multiple output points throughout the architecture** — each exit is a complete classifier, and the network can produce predictions at any exit point, enabling flexible accuracy-latency trade-offs at inference time. **Multi-Exit Design** - **Exit Architecture**: Each exit has its own pooling, feature transform, and classification head. - **Self-Distillation**: Later exits teach earlier exits through knowledge distillation — improves early exit quality. - **Training Strategies**: Weighted sum of all exit losses, curriculum learning, or gradient equilibrium. - **Orchestration**: At inference, choose the exit based on input difficulty, latency budget, or confidence threshold. **Why It Matters** - **Anytime Prediction**: Can produce a prediction at any time — interrupted computation still gives a result. - **Device Adaptation**: Same model serves different devices — powerful devices use all exits, weak devices exit early. - **Efficiency Scaling**: Linear relationship between exits used and compute — predictable resource usage. **Multi-Exit Networks** are **the Swiss Army knife of inference** — offering multiple accuracy-efficiency operating points within a single model.