Figure 5.

Given multiple ML models, the HIL of each can be fused together by repeating the same training procedure. Thus, given an image, each hashing network converts it to a different binary vector, which is projected into hyperdimensional lengths. These are bound with symbolic vectors identifying each individual hashing network and aggregated via consensus sum. The result allows us to perform inference across multiple models at testing time.