Figure 3.

The training pipeline for a particular class of “dog.” First, training images are hashed into binary vectors using a pre-trained network. The vectors for each image are then projected to a hyperdimensional length by randomly repeating the bits consistently. Each vector is aggregated by the consensus sum operation into a single vector containing all training instances for that class. A symbolic representation of the class, called “Dog” in this example, as another hyperdimensional vector, is bound to the aggregated vector. This forms the association between representative images and the class itself. Once these inference vectors are computed for each class, they are aggregated by consensus sum into the Hyperdimensional Inference Layer, which then performs classification at testing time.