Dynamic Resolution Networks are networks that adaptively choose the input or feature map resolution for each sample — processing easy images at low resolution (fast) and hard images at high resolution (accurate), optimizing the computation per sample based on difficulty.

Dynamic Resolution Methods

• Input Resolution: Downscale easy inputs before processing — less computation for smaller inputs.
• Feature Resolution: Use early features at low resolution, upscale only for hard cases.
• Multi-Scale: Process at multiple resolutions and fuse — attend more to resolution levels that help.
• Resolution Policy: Train a lightweight policy network to select the optimal resolution per input.

Why It Matters

• Quadratic Savings: Computation in conv layers scales quadratically with spatial resolution — halving resolution gives 4× speedup.
• Natural Hierarchy: Many images have easy-to-classify global structure — low resolution suffices.
• Defect Inspection: Large wafer images with localized defects don't need full-resolution processing everywhere.

Dynamic Resolution is zooming in only where needed — adapting spatial resolution to each input's complexity for efficient image processing.