• Corpus ID: 202558574

Semantic Similarity Based Softmax Classifier for Zero-Shot Learning

@article{Daghaghi2019SemanticSB,
  title={Semantic Similarity Based Softmax Classifier for Zero-Shot Learning},
  author={Shabnam Daghaghi and Tharun Medini and Anshumali Shrivastava},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.04790}
}
Zero-Shot Learning (ZSL) is a classification task where we do not have even a single training labeled example from a set of unseen classes. Instead, we only have prior information (or description) about seen and unseen classes, often in the form of physically realizable or descriptive attributes. Lack of any single training example from a set of classes prohibits use of standard classification techniques and losses, including the popular crossentropy loss. Currently, state-of-the-art approaches… 
A Review of Generalized Zero-Shot Learning Methods
TLDR
This review paper presents a hierarchical categorization of the GZSL methods and discusses the representative methods of each category, and discusses several research directions for future studies.

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