Zero-Shot Learning via Semantic Similarity Embedding

@article{Zhang2015ZeroShotLV,
  title={Zero-Shot Learning via Semantic Similarity Embedding},
  author={Ziming Zhang and Venkatesh Saligrama},
  journal={2015 IEEE International Conference on Computer Vision (ICCV)},
  year={2015},
  pages={4166-4174}
}
In this paper we consider a version of the zero-shot learning problem where seen class source and target domain data are provided. The goal during test-time is to accurately predict the class label of an unseen target domain instance based on revealed source domain side information (e.g. attributes) for unseen classes. Our method is based on viewing each source or target data as a mixture of seen class proportions and we postulate that the mixture patterns have to be similar if the two… CONTINUE READING
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