What is dynamic inference in computer vision models?

Dynamic inference adapts computation during runtime based on input complexity.

Dynamic inference methods adjust model execution paths depending on input difficulty. This includes skipping layers, early exiting, adaptive token pruning, or conditional computation. The goal is to reduce compute for easy samples while allocating more resources to harder ones, improving efficiency without sacrificing accuracy.