resolution-adaptive networks

**Resolution-Adaptive Networks** are **neural networks designed to operate effectively across a wide range of input resolutions** — a single model handles inputs from low to high resolution, adapting its processing to the available resolution without requiring separate models for each resolution. **Resolution Adaptation Methods** - **Multi-Scale Training**: Train on inputs at various resolutions — model learns to handle any resolution. - **Resolution-Dependent Channels**: Allocate more channels at higher resolutions for proportional compute scaling. - **Feature Pyramid Networks (FPN)**: Multi-resolution feature extraction with top-down and lateral connections. - **Resolution Policy**: Lightweight module decides the optimal resolution for each input. **Why It Matters** - **Flexible Input**: Real-world inputs come at varying resolutions — sensors, cameras, and equipment produce different resolutions. - **Efficiency**: Low-resolution inference for simple cases saves 4-16× computation (quadratic scaling). - **Quality Scaling**: When more compute is available, process at higher resolution for better accuracy. **Resolution-Adaptive Networks** are **scale-agnostic models** — handling any input resolution within a single network for flexible, efficient inference.