dynamic width networks

**Dynamic Width Networks** are **neural networks that adaptively select how many channels or neurons are active in each layer for each input** — using fewer channels for simple inputs and more for complex ones, providing a continuous trade-off between accuracy and computation. **Dynamic Width Methods** - **Slimmable Networks**: Train a single network to operate at multiple preset widths (0.25×, 0.5×, 0.75×, 1.0×). - **Channel Gating**: Learn binary gates to activate/deactivate channels per input. - **Width Multiplier**: MobileNet-style uniform width scaling across all layers. - **Attention-Based**: Use attention mechanisms to softly select channels. **Why It Matters** - **Hardware-Friendly**: Changing width maps directly to computation reduction on hardware (fewer MACs, less memory). - **Single Model**: One trained model serves multiple width settings — no need to train separate models. - **Smooth Trade-Off**: Width provides a smooth, continuous accuracy-efficiency trade-off. **Dynamic Width** is **adjusting the neural channel count** — using more neurons for hard inputs and fewer for easy ones within a single flexible network.