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Preserving Semantic Relations for Zero-Shot Learning
[article]
2018
arXiv
pre-print
Zero-shot learning has gained popularity due to its potential to scale recognition models without requiring additional training data. This is usually achieved by associating categories with their semantic information like attributes. However, we believe that the potential offered by this paradigm is not yet fully exploited. In this work, we propose to utilize the structure of the space spanned by the attributes using a set of relations. We devise objective functions to preserve these relations
arXiv:1803.03049v1
fatcat:kjz5nyrur5apbdtt7445yh3i7y