branchynet

**BranchyNet** is one of the **pioneering early exit network architectures** — introducing side branch classifiers at intermediate layers of a deep neural network, enabling fast inference for easy samples while maintaining accuracy for difficult samples through the full network. **BranchyNet Architecture** - **Main Network**: Standard deep CNN (VGG, ResNet, etc.) as the backbone. - **Branches**: Lightweight classifier branches attached at selected intermediate layers. - **Entropy Criterion**: Exit at a branch if the prediction entropy is below a threshold — low entropy = high confidence. - **Joint Training**: All branches and the main network are trained end-to-end with a combined loss. **Why It Matters** - **Foundational**: One of the first works to formalize early exit in deep networks for adaptive inference. - **Speedup**: 2-5× inference speedup for easy samples with minimal accuracy loss. - **Influence**: Inspired MSDNet, SCAN, and many subsequent adaptive inference architectures. **BranchyNet** is **the original early exit network** — pioneering the idea of attaching intermediate classifiers for input-adaptive, efficient inference.