Learning Transfers: Kan Extensions of Topological Invariants of Learned Representations
The paper introduces a mathematical framework to quantify how well a learned representation transfers between different tasks. It defines 'transfer discrepancy' as the largest distance between an invariant forced by task change and the actual observed target invariant. This allows researchers to measure structural transfer, moving beyond simple accuracy scores to understand what part of the source structure is preserved.