Zero-shot Classification using Hyperdimensional Computing
Samuele Ruffino Geethan Karunaratne Michael Hersche Luca Benini Abu Sebastian Abbas Rahimi
Classification based on Zero-shot Learning (ZSL) is the ability of a model to
classify inputs into novel classes on which the model has not previously seen
any training examples. Providing an auxiliary descriptor in the form of a set
of attributes describing the new classes involved in the ZSL-based
classification is one of the favored approaches to solving this challenging
task. In this work, inspired by Hyperdimensional Computing (HDC), we propose
the use of stationary binary codebooks of symbol-like distributed
representations inside an attribute encoder to compactly represent a
computationally simple end-to-end trainable model, which we name
Hyperdimensional Computing Zero-shot Classifier~(HDC-ZSC). It consists of a
trainable image encoder, an attribute encoder based on HDC, and a similarity
kernel. We show that HDC-ZSC can be used to first perform zero-shot attribute
extraction tasks and, can later be repurposed for Zero-shot Classification
tasks with minimal architectural changes and minimal model retraining. HDC-ZSC
achieves Pareto optimal results with a 63.8% top-1 classification accuracy on
the CUB-200 dataset by having only 26.6 million trainable parameters. Compared
to two other state-of-the-art non-generative approaches, HDC-ZSC achieves 4.3%
and 9.9% better accuracy, while they require more than 1.85x and 1.72x
parameters compared to HDC-ZSC, respectively.