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arXiv · 1502.08039

Probabilistic Zero-shot Classification with Semantic Rankings

Jihun Hamm·Mikhail Belkin
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Abstract

In this paper we propose a non-metric ranking-based representation of semantic similarity that allows natural aggregation of semantic information from multiple heterogeneous sources. We apply the ranking-based representation to zero-shot learning problems, and present deterministic and probabilistic zero-shot classifiers which can be built from pre-trained classifiers without retraining. We demonstrate their the advantages on two large real-world image datasets. In particular, we show that aggregating different sources of semantic information, including crowd-sourcing, leads to more accurate classification.

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Jihun Hamm, Mikhail Belkin. 2015-02-27. Probabilistic Zero-shot Classification with Semantic Rankings. https://arxiv.org/abs/1502.08039

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