Zero-Shot Classification with Discriminative Semantic Representation Learning

@article{Ye2017ZeroShotCW,
  title={Zero-Shot Classification with Discriminative Semantic Representation Learning},
  author={Meng Ye and Yuhong Guo},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={5103-5111}
}
Zero-shot learning, a special case of unsupervised domain adaptation where the source and target domains have disjoint label spaces, has become increasingly popular in the computer vision community. In this paper, we propose a novel zero-shot learning method based on discriminative sparse non-negative matrix factorization. The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the… CONTINUE READING
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